-----------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/ben/Dropbox/G7/Paper/ISQ/Revision/ConditionalAcceptance/Acceptance/ReplicationFiles/Feb2024G7Replication_CormierHeinzelRein
> sberg.log
  log type:  text
 opened on:  25 Feb 2024, 13:11:16

. use "/Users/ben/Dropbox/G7/Paper/ISQ/Revision/ConditionalAcceptance/Acceptance/ReplicationFiles/CRSg7frame.dta"

. do "/var/folders/pr/h4w2r5k51bl349y890ld5xjw0000gn/T//SD56449.000000"

. xtset policyArea_num year
       panel variable:  policyArea_num (strongly balanced)
        time variable:  year, 1975 to 2021
                delta:  1 unit

. *drop 4 G7RG categories don't use
. drop if PolicyArea == "East-West (Russia)" | PolicyArea == "IFI/UN reform" | PolicyArea == "International cooperation" | PolicyArea == "Devel
> opment"
(188 observations deleted)

. *************************************************************************************************************************
. *Figure 2: Annual Variation in Commitment Dummy by Policy Area (30 boxes)
. set scheme s1mono

. grstyle init

. grstyle set plain, nogrid

. *Commit Count Version
. twoway connected NoOfG7Commits year, by(PolicyArea, note ("")) ///
>         ytitle("Number of Commitments") ytitle(, size(small)) ///
>         xtitle("") xtitle(, size(small)) xlabel(1975(15)2021)

. 
. *************************************************************************************************************************
. *Table 2 (Spend)
. *Model 1 (Correlation)
. reghdfe deltaCommitSpend CommitNoIncreaseDummy ///
> , absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      1,296
Absorbing 2 HDFE groups                           F(   1,     28) =       3.13
Statistics robust to heteroskedasticity           Prob > F        =     0.0877
                                                  R-squared       =     0.1043
                                                  Adj R-squared   =     0.0493
                                                  Within R-sq.    =     0.0009
Number of clusters (policyArea_num) =         29  Root MSE        =   397.9317

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   29.64667   16.75273     1.77   0.088    -4.669748    63.96308
                _cons |  -7.864171    4.42086    -1.78   0.086    -16.91989    1.191549
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        46           0          46     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model1

. *Model 2 (Add Controls)
. reghdfe deltaCommitSpend CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      1,268
Absorbing 2 HDFE groups                           F(   3,     28) =      24.04
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1504
                                                  Adj R-squared   =     0.0962
                                                  Within R-sq.    =     0.0524
Number of clusters (policyArea_num) =         29  Root MSE        =   392.2392

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   33.40983   16.98723     1.97   0.059    -1.386936    68.20659
                      |
     deltaCommitSpend |
                  L1. |   -.224248   .0616932    -3.63   0.001    -.3506209   -.0978752
                      |
   AllNewTFs_ThisArea |
                  L1. |  -8.622424   12.22393    -0.71   0.486    -33.66201    16.41716
                      |
                _cons |   5.510812   20.31916     0.27   0.788    -36.11111    47.13273
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        45           0          45     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model2

. *Model 3 (All Controls)
. reghdfe deltaCommitSpend CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      1,268
Absorbing 2 HDFE groups                           F(   4,     28) =      18.19
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1505
                                                  Adj R-squared   =     0.0955
                                                  Within R-sq.    =     0.0524
Number of clusters (policyArea_num) =         29  Root MSE        =   392.3952

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   33.22877    16.7805     1.98   0.058    -1.144528    67.60208
                      |
     deltaCommitSpend |
                  L1. |  -.2241499   .0619352    -3.62   0.001    -.3510183   -.0972814
                      |
   AllNewTFs_ThisArea |
                  L1. |  -9.597041   16.97694    -0.57   0.576    -44.37272    25.17863
                      |
  cumsumTFsInThisArea |
                  L1. |   .1542438    .866651     0.18   0.860     -1.62101    1.929498
                      |
                _cons |   3.334116   12.94714     0.26   0.799     -23.1869    29.85513
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        45           0          45     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model3

. *Table
. esttab Model1 Model2 Model3 using Tab2_SpendMain.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> title("Table 2: Estimating the G7 Effect on Aid Spending") /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file Tab2_SpendMain.csv not found)
(output written to Tab2_SpendMain.csv)

. 
. *************************************************************************************************************************
. *Table 3 and Figure 3 (Spend placebos)
. *Model 4 (1 lead and lag)
. reghdfe deltaCommitSpend l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      1,234
Absorbing 2 HDFE groups                           F(   6,     28) =      16.47
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1773
                                                  Adj R-squared   =     0.1217
                                                  Within R-sq.    =     0.0772
Number of clusters (policyArea_num) =         29  Root MSE        =   385.7968

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L1. |   6.694798   33.51916     0.20   0.843    -61.96609    75.35569
                  --. |   33.70103   14.41903     2.34   0.027     4.164992    63.23706
                  F1. |   26.64803   30.11294     0.88   0.384    -35.03553    88.33159
                      |
     deltaCommitSpend |
                  L1. |  -.3115787   .0822365    -3.79   0.001    -.4800326   -.1431248
                      |
   AllNewTFs_ThisArea |
                  L1. |  -17.59847   16.62004    -1.06   0.299    -51.64308    16.44614
                      |
  cumsumTFsInThisArea |
                  L1. |   .6698259   .8678263     0.77   0.447    -1.107836    2.447487
                      |
                _cons |  -1.315488   15.25538    -0.09   0.932    -32.56471    29.93373
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        44           0          44     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model4

. *Model 5 (2 deep leads and lags)
. reghdfe deltaCommitSpend l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDummy f2.CommitNoIncreaseDu
> mmy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,172
Absorbing 2 HDFE groups                           F(   8,     28) =      23.40
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1182
                                                  Adj R-squared   =     0.0552
                                                  Within R-sq.    =     0.0643
Number of clusters (policyArea_num) =         29  Root MSE        =   329.9254

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L2. |  -13.41749   18.09924    -0.74   0.465    -50.49209    23.65712
                  L1. |   2.282586   34.35583     0.07   0.948    -68.09215    72.65732
                  --. |   26.50175    14.5592     1.82   0.079    -3.321421    56.32493
                  F1. |   26.81653   19.79719     1.35   0.186    -13.73618    67.36924
                  F2. |   26.62662   27.15344     0.98   0.335    -28.99467    82.24791
                      |
     deltaCommitSpend |
                  L1. |  -.2452789   .0696491    -3.52   0.001    -.3879487   -.1026091
                      |
   AllNewTFs_ThisArea |
                  L1. |  -5.672596   5.649967    -1.00   0.324    -17.24603    5.900837
                      |
  cumsumTFsInThisArea |
                  L1. |   .6106272   .3712675     1.64   0.111    -.1498797    1.371134
                      |
                _cons |  -1.903944   11.13339    -0.17   0.865    -24.70967    20.90178
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        42           0          42     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model5

. *Model 6 (3 deep leads and lags)
. reghdfe deltaCommitSpend l3.CommitNoIncreaseDummy l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDu
> mmy f2.CommitNoIncreaseDummy f3.CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,110
Absorbing 2 HDFE groups                           F(  10,     28) =      16.50
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1186
                                                  Adj R-squared   =     0.0519
                                                  Within R-sq.    =     0.0637
Number of clusters (policyArea_num) =         29  Root MSE        =   334.6840

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L3. |  -3.877404   21.31564    -0.18   0.857    -47.54052    39.78571
                  L2. |  -11.74735   18.86842    -0.62   0.539    -50.39755    26.90285
                  L1. |  -3.893334   38.53987    -0.10   0.920    -82.83868    75.05201
                  --. |   28.49576   14.55561     1.96   0.060    -1.320068    58.31158
                  F1. |   23.78609   20.26265     1.17   0.250    -17.72007    65.29225
                  F2. |   30.71748   30.04591     1.02   0.315    -30.82878    92.26374
                  F3. |   8.281736   10.81424     0.77   0.450    -13.87022     30.4337
                      |
     deltaCommitSpend |
                  L1. |  -.2523158    .068782    -3.67   0.001    -.3932093   -.1114223
                      |
   AllNewTFs_ThisArea |
                  L1. |  -8.658525   6.652689    -1.30   0.204    -22.28594    4.968891
                      |
  cumsumTFsInThisArea |
                  L1. |   .7694596   .4561501     1.69   0.103    -.1649216    1.703841
                      |
                _cons |  -4.109913   15.07174    -0.27   0.787    -34.98298    26.76315
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        40           0          40     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model6

. 
. *Table
. esttab Model4 Model5 Model6 using Tab3_SpendPlacebos.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file Tab3_SpendPlacebos.csv not found)
(output written to Tab3_SpendPlacebos.csv)

. 
. 
. *Figure 3 (Placebo AMEs)
. *Label coefs/vars for coefplot
. label variable CommitNoIncreaseDummy "G7Commit" 

. coefplot Model4 Model5 Model6, keep(*CommitNoIncreaseDummy l.CommitNoIncreaseDummy f.CommitNoIncreaseDummy l2.CommitNoIncreaseDummy f2.Commit
> NoIncreaseDummy l3.CommitNoIncreaseDummy f3.CommitNoIncreaseDummy) ///
> order(l3.CommitNoIncreaseDummy l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDummy f2.CommitNoIncr
> easeDummy f3.CommitNoIncreaseDummy) ///
> coeflabels(L.CommitNoIncreaseDummy = "G7Commit_t-1" F.CommitNoIncreaseDummy = "G7Commit_t+1" L2.CommitNoIncreaseDummy = "G7Commit_t-2" F2.Com
> mitNoIncreaseDummy = "G7Commit_t+2" L3.CommitNoIncreaseDummy = "G7Commit_t-3" F3.CommitNoIncreaseDummy = "G7Commit_t+3") ///
> xline(0, lc(black)) mcolor(mono) legend(size(small) rows(1)) levels(90) ///
> title("Fig. 3: Table 3 G7Commit Lead/Lag Placebo Tests", size(mediumsmall)) note("90% confidence intervals")
(note:  named style mono not found in class color, default attributes used)
(note:  named style mono not found in class color, default attributes used)
(note:  named style mono not found in class color, default attributes used)
(note:  named style mono not found in class color, default attributes used)
(note:  named style mono not found in class color, default attributes used)
(note:  named style mono not found in class color, default attributes used)
(note:  named style mediumsmall not found in class gsize, default attributes used)

. 
. *************************************************************************************************************************
. *Table 4 (Trust Funds)
. *Model 7 (Correlation)
. ppmlhdfe AllNewTFs_ThisArea CommitNoIncreaseDummy ///
> , absorb(policyArea_num year) cluster(policyArea_num)
(dropped 56 observations that are either singletons or separated by a fixed effect)
Iteration 1:   deviance = 1.1239e+03  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -3.19  P   
Iteration 2:   deviance = 9.5623e+02  eps = 1.75e-01  iters = 3    tol = 1.0e-04  min(eta) =  -4.87      
Iteration 3:   deviance = 9.2712e+02  eps = 3.14e-02  iters = 2    tol = 1.0e-04  min(eta) =  -6.39      
Iteration 4:   deviance = 9.2332e+02  eps = 4.12e-03  iters = 2    tol = 1.0e-04  min(eta) =  -7.41      
Iteration 5:   deviance = 9.2313e+02  eps = 2.01e-04  iters = 2    tol = 1.0e-04  min(eta) =  -7.79      
Iteration 6:   deviance = 9.2313e+02  eps = 1.37e-06  iters = 2    tol = 1.0e-04  min(eta) =  -7.83      
Iteration 7:   deviance = 9.2313e+02  eps = 1.83e-10  iters = 2    tol = 1.0e-05  min(eta) =  -7.83   S  
Iteration 8:   deviance = 9.2313e+02  eps = 0.00e+00  iters = 2    tol = 1.0e-07  min(eta) =  -7.83   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 8 iterations and 19 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =      1,240
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(1)    =       3.93
Deviance             =  923.1304793               Prob > chi2     =     0.0474
Log pseudolikelihood = -1429.791159               Pseudo R2       =     0.4824

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .1067649   .0538483     1.98   0.047     .0012242    .2123056
                _cons |   1.139554   .0168307    67.71   0.000     1.106567    1.172542
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        44           0          44     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model7

. *Model 8 (Add Controls)
. ppmlhdfe AllNewTFs_ThisArea CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(dropped 56 observations that are either singletons or separated by a fixed effect)
Iteration 1:   deviance = 1.1174e+03  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -3.12  P   
Iteration 2:   deviance = 9.4583e+02  eps = 1.81e-01  iters = 3    tol = 1.0e-04  min(eta) =  -4.91      
Iteration 3:   deviance = 9.1730e+02  eps = 3.11e-02  iters = 3    tol = 1.0e-04  min(eta) =  -6.46      
Iteration 4:   deviance = 9.1372e+02  eps = 3.92e-03  iters = 2    tol = 1.0e-04  min(eta) =  -7.46      
Iteration 5:   deviance = 9.1355e+02  eps = 1.84e-04  iters = 2    tol = 1.0e-04  min(eta) =  -7.83      
Iteration 6:   deviance = 9.1355e+02  eps = 1.20e-06  iters = 2    tol = 1.0e-04  min(eta) =  -7.86      
Iteration 7:   deviance = 9.1355e+02  eps = 1.58e-10  iters = 2    tol = 1.0e-05  min(eta) =  -7.87   S  
Iteration 8:   deviance = 9.1355e+02  eps = 0.00e+00  iters = 2    tol = 1.0e-07  min(eta) =  -7.87   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 8 iterations and 20 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =      1,212
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(3)    =       6.06
Deviance             =  913.5488015               Prob > chi2     =     0.1088
Log pseudolikelihood =  -1414.00032               Pseudo R2       =     0.4800

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .1088209   .0526204     2.07   0.039     .0056869    .2119549
                      |
     deltaCommitSpend |
                  L1. |   .0000699   .0000432     1.62   0.106    -.0000148    .0001545
                      |
   AllNewTFs_ThisArea |
                  L1. |  -.0118606   .0155012    -0.77   0.444    -.0422423    .0185212
                      |
                _cons |   1.189233   .0626755    18.97   0.000     1.066392    1.312075
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        43           0          43     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model8

. *Model 9 (All Controls)
. ppmlhdfe AllNewTFs_ThisArea CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(dropped 56 observations that are either singletons or separated by a fixed effect)
Iteration 1:   deviance = 1.1046e+03  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -3.16  P   
Iteration 2:   deviance = 8.8798e+02  eps = 2.44e-01  iters = 3    tol = 1.0e-04  min(eta) =  -5.25      
Iteration 3:   deviance = 8.4758e+02  eps = 4.77e-02  iters = 3    tol = 1.0e-04  min(eta) =  -7.05      
Iteration 4:   deviance = 8.4298e+02  eps = 5.45e-03  iters = 3    tol = 1.0e-04  min(eta) =  -8.15      
Iteration 5:   deviance = 8.4276e+02  eps = 2.67e-04  iters = 3    tol = 1.0e-04  min(eta) =  -8.56      
Iteration 6:   deviance = 8.4275e+02  eps = 2.06e-06  iters = 2    tol = 1.0e-04  min(eta) =  -8.61      
Iteration 7:   deviance = 8.4275e+02  eps = 3.77e-10  iters = 2    tol = 1.0e-05  min(eta) =  -8.61   S  
Iteration 8:   deviance = 8.4275e+02  eps = 0.00e+00  iters = 2    tol = 1.0e-07  min(eta) =  -8.61   S  
Iteration 9:   deviance = 8.4275e+02  eps = 0.00e+00  iters = 1    tol = 1.0e-09  min(eta) =  -8.61   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 9 iterations and 23 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =      1,212
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(4)    =     113.43
Deviance             =  842.7536736               Prob > chi2     =     0.0000
Log pseudolikelihood = -1378.602756               Pseudo R2       =     0.4930

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .1039019   .0499911     2.08   0.038     .0059212    .2018826
                      |
     deltaCommitSpend |
                  L1. |   .0000717   .0000406     1.77   0.077    -7.88e-06    .0001514
                      |
   AllNewTFs_ThisArea |
                  L1. |   .0193566   .0126228     1.53   0.125    -.0053836    .0440968
                      |
  cumsumTFsInThisArea |
                  L1. |  -.0136035   .0013732    -9.91   0.000    -.0162949   -.0109121
                      |
                _cons |   1.877606   .0764276    24.57   0.000     1.727811    2.027401
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        43           0          43     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model9

. *Table
. esttab Model7 Model8 Model9 using Tab4_TFMain.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> title("Table 4: Estimating the G7 Effect on Trsut Funds") /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file Tab4_TFMain.csv not found)
(output written to Tab4_TFMain.csv)

. 
. *************************************************************************************************************************
. *Table 5 and Figure 4 (TFund placebos)
. *Model 10 (1 lead and lag)
. ppmlhdfe AllNewTFs_ThisArea l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(dropped 56 observations that are either singletons or separated by a fixed effect)
Iteration 1:   deviance = 1.0409e+03  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -3.13  P   
Iteration 2:   deviance = 8.2579e+02  eps = 2.60e-01  iters = 3    tol = 1.0e-04  min(eta) =  -5.29      
Iteration 3:   deviance = 7.8453e+02  eps = 5.26e-02  iters = 3    tol = 1.0e-04  min(eta) =  -7.19      
Iteration 4:   deviance = 7.7927e+02  eps = 6.75e-03  iters = 3    tol = 1.0e-04  min(eta) =  -8.49      
Iteration 5:   deviance = 7.7887e+02  eps = 5.15e-04  iters = 3    tol = 1.0e-04  min(eta) =  -9.10      
Iteration 6:   deviance = 7.7886e+02  eps = 1.33e-05  iters = 2    tol = 1.0e-04  min(eta) =  -9.23      
Iteration 7:   deviance = 7.7886e+02  eps = 2.37e-08  iters = 2    tol = 1.0e-05  min(eta) =  -9.23   S  
Iteration 8:   deviance = 7.7886e+02  eps = 1.06e-13  iters = 2    tol = 1.0e-06  min(eta) =  -9.23   S  
Iteration 9:   deviance = 7.7886e+02  eps = 1.70e-16  iters = 2    tol = 1.0e-09  min(eta) =  -9.23   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 9 iterations and 24 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =      1,178
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(6)    =     130.53
Deviance             =  778.8590483               Prob > chi2     =     0.0000
Log pseudolikelihood = -1329.872151               Pseudo R2       =     0.4990

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L1. |   .0805875   .0432064     1.87   0.062    -.0040955    .1652704
                  --. |   .1283286   .0535379     2.40   0.017     .0233961     .233261
                  F1. |   .0397342   .0443359     0.90   0.370    -.0471626    .1266309
                      |
     deltaCommitSpend |
                  L1. |   .0000818   .0000421     1.94   0.052    -7.07e-07    .0001644
                      |
   AllNewTFs_ThisArea |
                  L1. |   .0060214   .0114949     0.52   0.600    -.0165081    .0285509
                      |
  cumsumTFsInThisArea |
                  L1. |  -.0126227   .0012505   -10.09   0.000    -.0150735   -.0101718
                      |
                _cons |   1.838895   .0775934    23.70   0.000     1.686815    1.990976
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        42           0          42     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model10

. *Model 11 (2 leads and lags)
. ppmlhdfe AllNewTFs_ThisArea l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDummy f2.CommitNoIncreas
> eDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(dropped 56 observations that are either singletons or separated by a fixed effect)
Iteration 1:   deviance = 9.7661e+02  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -3.13  P   
Iteration 2:   deviance = 7.7546e+02  eps = 2.59e-01  iters = 3    tol = 1.0e-04  min(eta) =  -5.29      
Iteration 3:   deviance = 7.3848e+02  eps = 5.01e-02  iters = 3    tol = 1.0e-04  min(eta) =  -7.19      
Iteration 4:   deviance = 7.3384e+02  eps = 6.33e-03  iters = 3    tol = 1.0e-04  min(eta) =  -8.48      
Iteration 5:   deviance = 7.3348e+02  eps = 4.98e-04  iters = 3    tol = 1.0e-04  min(eta) =  -9.09      
Iteration 6:   deviance = 7.3347e+02  eps = 1.24e-05  iters = 2    tol = 1.0e-04  min(eta) =  -9.21      
Iteration 7:   deviance = 7.3347e+02  eps = 1.86e-08  iters = 2    tol = 1.0e-05  min(eta) =  -9.21   S  
Iteration 8:   deviance = 7.3347e+02  eps = 5.95e-14  iters = 2    tol = 1.0e-06  min(eta) =  -9.21   S  
Iteration 9:   deviance = 7.3347e+02  eps = 0.00e+00  iters = 2    tol = 1.0e-09  min(eta) =  -9.21   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 9 iterations and 24 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =      1,116
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(8)    =     146.72
Deviance             =   733.466516               Prob > chi2     =     0.0000
Log pseudolikelihood = -1275.796578               Pseudo R2       =     0.4966

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L2. |  -.0036864   .0380409    -0.10   0.923    -.0782452    .0708724
                  L1. |   .0878209   .0450978     1.95   0.051    -.0005692    .1762109
                  --. |   .1293643   .0546703     2.37   0.018     .0222124    .2365162
                  F1. |    .011307   .0463872     0.24   0.807    -.0796103    .1022242
                  F2. |  -.0564641    .044508    -1.27   0.205    -.1436982      .03077
                      |
     deltaCommitSpend |
                  L1. |   .0000678   .0000427     1.59   0.113    -.0000159    .0001514
                      |
   AllNewTFs_ThisArea |
                  L1. |    .010279   .0129578     0.79   0.428    -.0151178    .0356758
                      |
  cumsumTFsInThisArea |
                  L1. |  -.0124067   .0012089   -10.26   0.000    -.0147762   -.0100372
                      |
                _cons |   1.840501   .0678342    27.13   0.000     1.707549    1.973454
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        40           0          40     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model11

. *Model 12 (3 leads and lags)
. ppmlhdfe AllNewTFs_ThisArea l3.CommitNoIncreaseDummy l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreas
> eDummy f2.CommitNoIncreaseDummy f3.CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(dropped 56 observations that are either singletons or separated by a fixed effect)
Iteration 1:   deviance = 9.2430e+02  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -3.14  P   
Iteration 2:   deviance = 7.3572e+02  eps = 2.56e-01  iters = 3    tol = 1.0e-04  min(eta) =  -5.31      
Iteration 3:   deviance = 7.0104e+02  eps = 4.95e-02  iters = 3    tol = 1.0e-04  min(eta) =  -7.19      
Iteration 4:   deviance = 6.9678e+02  eps = 6.11e-03  iters = 3    tol = 1.0e-04  min(eta) =  -8.47      
Iteration 5:   deviance = 6.9647e+02  eps = 4.56e-04  iters = 3    tol = 1.0e-04  min(eta) =  -9.05      
Iteration 6:   deviance = 6.9646e+02  eps = 9.98e-06  iters = 2    tol = 1.0e-04  min(eta) =  -9.16      
Iteration 7:   deviance = 6.9646e+02  eps = 1.07e-08  iters = 2    tol = 1.0e-05  min(eta) =  -9.16   S  
Iteration 8:   deviance = 6.9646e+02  eps = 1.83e-14  iters = 2    tol = 1.0e-07  min(eta) =  -9.16   S  
Iteration 9:   deviance = 6.9646e+02  eps = 1.90e-16  iters = 2    tol = 1.0e-09  min(eta) =  -9.16   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 9 iterations and 24 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =      1,054
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(10)   =     117.89
Deviance             =  696.4588013               Prob > chi2     =     0.0000
Log pseudolikelihood = -1199.422125               Pseudo R2       =     0.4959

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L3. |  -.0406795   .0426499    -0.95   0.340    -.1242717    .0429127
                  L2. |  -.0098683    .041802    -0.24   0.813    -.0917988    .0720622
                  L1. |   .0923588   .0457468     2.02   0.043     .0026968    .1820208
                  --. |   .1204024   .0544584     2.21   0.027      .013666    .2271389
                  F1. |   .0032726   .0498304     0.07   0.948    -.0943933    .1009385
                  F2. |  -.0532203   .0474182    -1.12   0.262    -.1461582    .0397176
                  F3. |   .0043449   .0378564     0.11   0.909    -.0698524    .0785422
                      |
     deltaCommitSpend |
                  L1. |   .0000647   .0000363     1.78   0.075    -6.43e-06    .0001358
                      |
   AllNewTFs_ThisArea |
                  L1. |   .0000651   .0133282     0.00   0.996    -.0260577     .026188
                      |
  cumsumTFsInThisArea |
                  L1. |  -.0124944   .0014824    -8.43   0.000    -.0153999   -.0095888
                      |
                _cons |   1.859469   .0875196    21.25   0.000     1.687934    2.031004
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        38           0          38     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model12

. *Table
. esttab Model10 Model11 Model12 using Tab5_TFMain_Placebos.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> title("Table 5: Estimating the G7 Effect on Trsut Funds") /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file Tab5_TFMain_Placebos.csv not found)
(output written to Tab5_TFMain_Placebos.csv)

. 
. 
. *Figure 4 (Placebo AMEs)
. coefplot Model10 Model11 Model12, keep(*CommitNoIncreaseDummy l.CommitNoIncreaseDummy f.CommitNoIncreaseDummy l2.CommitNoIncreaseDummy f2.Com
> mitNoIncreaseDummy l3.CommitNoIncreaseDummy f3.CommitNoIncreaseDummy) ///
> order(l3.CommitNoIncreaseDummy l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDummy f2.CommitNoIncr
> easeDummy f3.CommitNoIncreaseDummy) ///
> coeflabels(L.CommitNoIncreaseDummy = "G7Commit_t-1" F.CommitNoIncreaseDummy = "G7Commit_t+1" L2.CommitNoIncreaseDummy = "G7Commit_t-2" F2.Com
> mitNoIncreaseDummy = "G7Commit_t+2" L3.CommitNoIncreaseDummy = "G7Commit_t-3" F3.CommitNoIncreaseDummy = "G7Commit_t+3") ///
> xline(0, lc(black)) mcolor(mono) legend(size(small) rows(1)) levels(90) ///
> title("Fig. 4: Table 5 G7Commit Lead/Lag Placebo Tests", size(mediumsmall)) note("90% confidence intervals")
(note:  named style mono not found in class color, default attributes used)
(note:  named style mono not found in class color, default attributes used)
(note:  named style mono not found in class color, default attributes used)
(note:  named style mono not found in class color, default attributes used)
(note:  named style mono not found in class color, default attributes used)
(note:  named style mono not found in class color, default attributes used)
(note:  named style mediumsmall not found in class gsize, default attributes used)

. 
. *************************************************************************************************************************
. * Appendix A (Descriptives)
. tabstat CRSspendOnG7Commit deltaCommitSpend SpendDV_minusHost deltaSpendNoHost AllNewTFs_ThisArea CommitNoIncreaseDummy cumsumTFsInThisArea, 
> stat(n mean mi ma sd) case col(stat)

    variable |         N      mean       min       max        sd
-------------+--------------------------------------------------
CRSspendOn~t |      1296  198.0006         0  8650.742  699.5749
deltaCommi~d |      1296 -.0407455 -6258.444   4112.69  408.1102
SpendDV_mi~t |      1296  195.8169         0  8650.742  696.5679
deltaSpend~t |      1296 -.0407451 -6258.444  4112.689  412.9223
AllNewTFs_~a |      1296  1.671296         0        10  2.281362
CommitNoIn~y |      1296  .2638889         0         1  .4409099
cumsumTFsI~a |      1296  26.28241         0       155  38.54403
----------------------------------------------------------------

. 
. *************************************************************************************************************************
. * Appendix C (1990-2021 Sample)
. *Drop pre 1990
. drop if year < 1990
(450 observations deleted)

. 
. * App C Table 1
. *Model 1 (Correlation)
. reghdfe deltaCommitSpend CommitNoIncreaseDummy ///
> , absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =        904
Absorbing 2 HDFE groups                           F(   1,     28) =       3.20
Statistics robust to heteroskedasticity           Prob > F        =     0.0843
                                                  R-squared       =     0.1046
                                                  Adj R-squared   =     0.0398
                                                  Within R-sq.    =     0.0012
Number of clusters (policyArea_num) =         29  Root MSE        =   478.6065

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   37.32732    20.8529     1.79   0.084    -5.387912    80.04254
                _cons |    -12.676   6.597267    -1.92   0.065    -26.18989    .8378895
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        32           0          32     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model1

. *Model 2 (Add Controls)
. reghdfe deltaCommitSpend CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =        876
Absorbing 2 HDFE groups                           F(   3,     28) =      23.77
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1509
                                                  Adj R-squared   =     0.0861
                                                  Within R-sq.    =     0.0527
Number of clusters (policyArea_num) =         29  Root MSE        =   474.2710

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   43.40093    21.4098     2.03   0.052    -.4550571    87.25692
                      |
     deltaCommitSpend |
                  L1. |  -.2237691   .0614154    -3.64   0.001    -.3495729   -.0979654
                      |
   AllNewTFs_ThisArea |
                  L1. |  -11.02331   16.14824    -0.68   0.500    -44.10148    22.05485
                      |
                _cons |   10.15387   35.92804     0.28   0.780    -63.44138    83.74912
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        31           0          31     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model2

. *Model 3 (All Controls)
. reghdfe deltaCommitSpend CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =        876
Absorbing 2 HDFE groups                           F(   4,     28) =      17.86
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1509
                                                  Adj R-squared   =     0.0850
                                                  Within R-sq.    =     0.0527
Number of clusters (policyArea_num) =         29  Root MSE        =   474.5630

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   43.41263   21.17087     2.05   0.050     .0460599     86.7792
                      |
     deltaCommitSpend |
                  L1. |  -.2237716   .0616541    -3.63   0.001    -.3500644   -.0974789
                      |
   AllNewTFs_ThisArea |
                  L1. |  -11.00259   18.28495    -0.60   0.552    -48.45762    26.45243
                      |
  cumsumTFsInThisArea |
                  L1. |  -.0069046   .9759272    -0.01   0.994    -2.006001    1.992192
                      |
                _cons |   10.34552   27.04194     0.38   0.705    -45.04737    65.73842
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        31           0          31     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model3

. *Table
. esttab Model1 Model2 Model3 using Spend_19902021only.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> title("1990-2021 Spending") /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file Spend_19902021only.csv not found)
(output written to Spend_19902021only.csv)

. 
. * App C Table 3 (Spend placebos)
. * 1 lead and lag
. reghdfe deltaCommitSpend l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =        842
Absorbing 2 HDFE groups                           F(   6,     28) =      15.99
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1785
                                                  Adj R-squared   =     0.1109
                                                  Within R-sq.    =     0.0781
Number of clusters (policyArea_num) =         29  Root MSE        =   469.6244

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L1. |   8.778666   43.16286     0.20   0.840    -79.63644    97.19378
                  --. |   45.85646   17.87926     2.56   0.016     9.232465    82.48046
                  F1. |   37.45787   38.74532     0.97   0.342    -41.90833    116.8241
                      |
     deltaCommitSpend |
                  L1. |  -.3122414   .0821765    -3.80   0.001    -.4805723   -.1439105
                      |
   AllNewTFs_ThisArea |
                  L1. |  -19.96414   17.98339    -1.11   0.276    -56.80145    16.87318
                      |
  cumsumTFsInThisArea |
                  L1. |   .4793596   .9921598     0.48   0.633    -1.552988    2.511707
                      |
                _cons |   3.451874    29.6298     0.12   0.908    -57.24201    64.14576
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        30           0          30     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model4

. * 2 leads and lags
. reghdfe deltaCommitSpend l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDummy f2.CommitNoIncreaseDu
> mmy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =        780
Absorbing 2 HDFE groups                           F(   8,     28) =      24.92
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1223
                                                  Adj R-squared   =     0.0437
                                                  Within R-sq.    =     0.0666
Number of clusters (policyArea_num) =         29  Root MSE        =   406.1115

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L2. |   -16.8087   22.71517    -0.74   0.465    -63.33863    29.72122
                  L1. |  -.0283703   46.89902    -0.00   1.000    -96.09666    96.03992
                  --. |   32.45166   17.82454     1.82   0.079    -4.060257    68.96358
                  F1. |   35.83568   25.32826     1.41   0.168    -16.04691    87.71828
                  F2. |   34.54115   35.30797     0.98   0.336    -37.78396    106.8663
                      |
     deltaCommitSpend |
                  L1. |  -.2492258   .0702261    -3.55   0.001    -.3930774   -.1053742
                      |
   AllNewTFs_ThisArea |
                  L1. |   -6.17187   6.470006    -0.95   0.348    -19.42508    7.081336
                      |
  cumsumTFsInThisArea |
                  L1. |   .6825728   .4084129     1.67   0.106    -.1540231    1.519169
                      |
                _cons |  -5.601245   21.23613    -0.26   0.794     -49.1015    37.89901
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        28           0          28     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model5

. * 3 leads and lags
. reghdfe deltaCommitSpend l3.CommitNoIncreaseDummy l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDu
> mmy f2.CommitNoIncreaseDummy f3.CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =        718
Absorbing 2 HDFE groups                           F(  10,     28) =      20.06
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1221
                                                  Adj R-squared   =     0.0360
                                                  Within R-sq.    =     0.0658
Number of clusters (policyArea_num) =         29  Root MSE        =   418.6299

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L3. |  -3.403565   29.29077    -0.12   0.908    -63.40298    56.59585
                  L2. |  -15.13826   24.92451    -0.61   0.549    -66.19381    35.91729
                  L1. |  -9.410088   52.44008    -0.18   0.859    -116.8287    98.00854
                  --. |   34.56017   18.90397     1.83   0.078    -4.162861    73.28321
                  F1. |   33.75573   28.66241     1.18   0.249    -24.95656    92.46803
                  F2. |   43.97218   44.12936     1.00   0.328    -46.42271    134.3671
                  F3. |   11.55559   15.01324     0.77   0.448    -19.19764    42.30881
                      |
     deltaCommitSpend |
                  L1. |  -.2552444   .0694673    -3.67   0.001    -.3975417    -.112947
                      |
   AllNewTFs_ThisArea |
                  L1. |  -9.565599   7.598985    -1.26   0.218    -25.13141    6.000215
                      |
  cumsumTFsInThisArea |
                  L1. |   .7486577   .5066197     1.48   0.151    -.2891056    1.786421
                      |
                _cons |  -7.645162   29.03636    -0.26   0.794    -67.12345    51.83312
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        26           0          26     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model6

. *Table
. esttab Model4 Model5 Model6 using SpendPlacebos_19902021only.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> title("1990-2021 Spending Placebos") /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file SpendPlacebos_19902021only.csv not found)
(output written to SpendPlacebos_19902021only.csv)

. 
. * App C Table 2 (TFs)
. *Model 7 (Correlation)
. ppmlhdfe AllNewTFs_ThisArea CommitNoIncreaseDummy ///
> , absorb(policyArea_num year) cluster(policyArea_num)
Iteration 1:   deviance = 8.7942e+02  eps = .         iters = 3    tol = 1.0e-04  min(eta) =  -3.07  P   
Iteration 2:   deviance = 7.7186e+02  eps = 1.39e-01  iters = 3    tol = 1.0e-04  min(eta) =  -4.71      
Iteration 3:   deviance = 7.5461e+02  eps = 2.28e-02  iters = 2    tol = 1.0e-04  min(eta) =  -6.11      
Iteration 4:   deviance = 7.5263e+02  eps = 2.63e-03  iters = 2    tol = 1.0e-04  min(eta) =  -6.91      
Iteration 5:   deviance = 7.5256e+02  eps = 9.11e-05  iters = 2    tol = 1.0e-04  min(eta) =  -7.13      
Iteration 6:   deviance = 7.5256e+02  eps = 2.23e-07  iters = 2    tol = 1.0e-05  min(eta) =  -7.14      
Iteration 7:   deviance = 7.5256e+02  eps = 2.17e-12  iters = 2    tol = 1.0e-06  min(eta) =  -7.14   S  
Iteration 8:   deviance = 7.5256e+02  eps = 0.00e+00  iters = 2    tol = 1.0e-08  min(eta) =  -7.14   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 8 iterations and 18 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =        904
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(1)    =       3.02
Deviance             =  752.5620743               Prob > chi2     =     0.0822
Log pseudolikelihood = -1226.674012               Pseudo R2       =     0.4330

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .0899629   .0517667     1.74   0.082     -.011498    .1914238
                _cons |   1.236915   .0166641    74.23   0.000     1.204254    1.269576
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        32           0          32     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model7

. *Model 8 (Add Controls)
. ppmlhdfe AllNewTFs_ThisArea CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea, absorb(policyArea_num year) cluster(policyArea_num)
Iteration 1:   deviance = 8.5049e+02  eps = .         iters = 3    tol = 1.0e-04  min(eta) =  -2.67  P   
Iteration 2:   deviance = 7.5590e+02  eps = 1.25e-01  iters = 3    tol = 1.0e-04  min(eta) =  -4.01      
Iteration 3:   deviance = 7.4302e+02  eps = 1.73e-02  iters = 3    tol = 1.0e-04  min(eta) =  -5.00      
Iteration 4:   deviance = 7.4174e+02  eps = 1.72e-03  iters = 2    tol = 1.0e-04  min(eta) =  -5.45      
Iteration 5:   deviance = 7.4170e+02  eps = 5.00e-05  iters = 2    tol = 1.0e-04  min(eta) =  -5.56      
Iteration 6:   deviance = 7.4170e+02  eps = 1.00e-07  iters = 2    tol = 1.0e-05  min(eta) =  -5.57      
Iteration 7:   deviance = 7.4170e+02  eps = 7.90e-13  iters = 2    tol = 1.0e-06  min(eta) =  -5.57   S  
Iteration 8:   deviance = 7.4170e+02  eps = 0.00e+00  iters = 2    tol = 1.0e-08  min(eta) =  -5.57   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 8 iterations and 19 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =        876
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(3)    =       3.85
Deviance             =  741.7020067               Prob > chi2     =     0.2778
Log pseudolikelihood = -1219.243978               Pseudo R2       =     0.4199

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .0856551   .0508211     1.69   0.092    -.0139524    .1852626
                      |
     deltaCommitSpend |
                  L1. |    .000067   .0000423     1.58   0.114     -.000016    .0001499
                      |
   AllNewTFs_ThisArea |
                  L1. |   .0000965   .0156454     0.01   0.995    -.0305679    .0307609
                      |
                _cons |   1.239732   .0648173    19.13   0.000     1.112693    1.366772
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        31           0          31     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model8

. *Model 9 (All Controls)
. ppmlhdfe AllNewTFs_ThisArea CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
Iteration 1:   deviance = 8.1640e+02  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -2.81  P   
Iteration 2:   deviance = 6.9832e+02  eps = 1.69e-01  iters = 3    tol = 1.0e-04  min(eta) =  -4.42      
Iteration 3:   deviance = 6.8131e+02  eps = 2.50e-02  iters = 3    tol = 1.0e-04  min(eta) =  -5.60      
Iteration 4:   deviance = 6.7972e+02  eps = 2.33e-03  iters = 3    tol = 1.0e-04  min(eta) =  -6.11      
Iteration 5:   deviance = 6.7967e+02  eps = 7.50e-05  iters = 3    tol = 1.0e-04  min(eta) =  -6.23      
Iteration 6:   deviance = 6.7967e+02  eps = 1.91e-07  iters = 2    tol = 1.0e-05  min(eta) =  -6.24      
Iteration 7:   deviance = 6.7967e+02  eps = 2.52e-12  iters = 2    tol = 1.0e-06  min(eta) =  -6.24   S  
Iteration 8:   deviance = 6.7967e+02  eps = 2.09e-16  iters = 2    tol = 1.0e-08  min(eta) =  -6.24   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 8 iterations and 22 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =        876
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(4)    =     118.53
Deviance             =  679.6736199               Prob > chi2     =     0.0000
Log pseudolikelihood = -1188.229785               Pseudo R2       =     0.4346

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .0850598   .0475396     1.79   0.074    -.0081162    .1782358
                      |
     deltaCommitSpend |
                  L1. |   .0000714   .0000399     1.79   0.074    -6.84e-06    .0001497
                      |
   AllNewTFs_ThisArea |
                  L1. |   .0167188   .0133186     1.26   0.209    -.0093852    .0428228
                      |
  cumsumTFsInThisArea |
                  L1. |   -.014182   .0013493   -10.51   0.000    -.0168267   -.0115374
                      |
                _cons |   2.063103   .0821109    25.13   0.000     1.902168    2.224037
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        31           0          31     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model9

. *Table
. esttab Model7 Model8 Model9 using TF_19902021only.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> title("1990-2021 Spending") /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file TF_19902021only.csv not found)
(output written to TF_19902021only.csv)

. 
. 
. * App C Table 4 (TF placebos)
. * Model 10 (1 lead and lag)
. ppmlhdfe AllNewTFs_ThisArea l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
Iteration 1:   deviance = 7.5197e+02  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -2.74  P   
Iteration 2:   deviance = 6.3667e+02  eps = 1.81e-01  iters = 3    tol = 1.0e-04  min(eta) =  -4.42      
Iteration 3:   deviance = 6.1896e+02  eps = 2.86e-02  iters = 3    tol = 1.0e-04  min(eta) =  -5.72      
Iteration 4:   deviance = 6.1684e+02  eps = 3.43e-03  iters = 3    tol = 1.0e-04  min(eta) =  -6.44      
Iteration 5:   deviance = 6.1668e+02  eps = 2.64e-04  iters = 3    tol = 1.0e-04  min(eta) =  -6.75      
Iteration 6:   deviance = 6.1668e+02  eps = 5.73e-06  iters = 2    tol = 1.0e-04  min(eta) =  -6.81      
Iteration 7:   deviance = 6.1668e+02  eps = 4.67e-09  iters = 2    tol = 1.0e-05  min(eta) =  -6.81   S  
Iteration 8:   deviance = 6.1668e+02  eps = 3.12e-15  iters = 2    tol = 1.0e-07  min(eta) =  -6.81   S  
Iteration 9:   deviance = 6.1668e+02  eps = 3.47e-16  iters = 1    tol = 1.0e-09  min(eta) =  -6.81   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 9 iterations and 23 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =        842
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(6)    =     123.39
Deviance             =   616.675648               Prob > chi2     =     0.0000
Log pseudolikelihood = -1139.947507               Pseudo R2       =     0.4379

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L1. |   .0764534   .0433718     1.76   0.078    -.0085538    .1614605
                  --. |   .1099345   .0515889     2.13   0.033     .0088222    .2110468
                  F1. |   .0419566   .0428795     0.98   0.328    -.0420857     .125999
                      |
     deltaCommitSpend |
                  L1. |   .0000814   .0000407     2.00   0.045     1.65e-06    .0001611
                      |
   AllNewTFs_ThisArea |
                  L1. |   .0033353   .0119486     0.28   0.780    -.0200836    .0267543
                      |
  cumsumTFsInThisArea |
                  L1. |  -.0131959   .0012755   -10.35   0.000    -.0156957    -.010696
                      |
                _cons |   2.025081   .0820601    24.68   0.000     1.864246    2.185916
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        30           0          30     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model10

. * Model 11 (2 leads and lags)
. ppmlhdfe AllNewTFs_ThisArea l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDummy f2.CommitNoIncreas
> eDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
Iteration 1:   deviance = 7.0137e+02  eps = .         iters = 3    tol = 1.0e-04  min(eta) =  -2.71  P   
Iteration 2:   deviance = 5.9356e+02  eps = 1.82e-01  iters = 3    tol = 1.0e-04  min(eta) =  -4.41      
Iteration 3:   deviance = 5.7736e+02  eps = 2.81e-02  iters = 3    tol = 1.0e-04  min(eta) =  -5.70      
Iteration 4:   deviance = 5.7550e+02  eps = 3.24e-03  iters = 3    tol = 1.0e-04  min(eta) =  -6.40      
Iteration 5:   deviance = 5.7536e+02  eps = 2.37e-04  iters = 3    tol = 1.0e-04  min(eta) =  -6.69      
Iteration 6:   deviance = 5.7536e+02  eps = 4.57e-06  iters = 2    tol = 1.0e-04  min(eta) =  -6.74      
Iteration 7:   deviance = 5.7536e+02  eps = 2.80e-09  iters = 2    tol = 1.0e-05  min(eta) =  -6.74   S  
Iteration 8:   deviance = 5.7536e+02  eps = 1.12e-15  iters = 2    tol = 1.0e-07  min(eta) =  -6.74   S  
Iteration 9:   deviance = 5.7536e+02  eps = 0.00e+00  iters = 1    tol = 1.0e-09  min(eta) =  -6.74   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 9 iterations and 22 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =        780
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(8)    =      90.39
Deviance             =  575.3572669               Prob > chi2     =     0.0000
Log pseudolikelihood =  -1069.90627               Pseudo R2       =     0.4351

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L2. |  -.0131339   .0384551    -0.34   0.733    -.0885046    .0622368
                  L1. |   .0784928   .0450167     1.74   0.081    -.0097383    .1667239
                  --. |    .109999   .0512986     2.14   0.032     .0094555    .2105424
                  F1. |   .0127408   .0467312     0.27   0.785    -.0788506    .1043323
                  F2. |  -.0546346   .0459765    -1.19   0.235     -.144747    .0354778
                      |
     deltaCommitSpend |
                  L1. |   .0000671   .0000425     1.58   0.114    -.0000161    .0001503
                      |
   AllNewTFs_ThisArea |
                  L1. |   .0129675    .013331     0.97   0.331    -.0131607    .0390956
                      |
  cumsumTFsInThisArea |
                  L1. |  -.0117139   .0013164    -8.90   0.000    -.0142941   -.0091338
                      |
                _cons |   1.946135   .0847507    22.96   0.000     1.780027    2.112244
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        28           0          28     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model11

. * Model 12 (3 leads and lags)
. ppmlhdfe AllNewTFs_ThisArea l3.CommitNoIncreaseDummy l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreas
> eDummy f2.CommitNoIncreaseDummy f3.CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
Iteration 1:   deviance = 6.5530e+02  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -2.71  P   
Iteration 2:   deviance = 5.5766e+02  eps = 1.75e-01  iters = 3    tol = 1.0e-04  min(eta) =  -4.41      
Iteration 3:   deviance = 5.4334e+02  eps = 2.63e-02  iters = 3    tol = 1.0e-04  min(eta) =  -5.69      
Iteration 4:   deviance = 5.4180e+02  eps = 2.86e-03  iters = 3    tol = 1.0e-04  min(eta) =  -6.37      
Iteration 5:   deviance = 5.4169e+02  eps = 1.91e-04  iters = 3    tol = 1.0e-04  min(eta) =  -6.63      
Iteration 6:   deviance = 5.4169e+02  eps = 3.01e-06  iters = 2    tol = 1.0e-04  min(eta) =  -6.67      
Iteration 7:   deviance = 5.4169e+02  eps = 1.16e-09  iters = 2    tol = 1.0e-05  min(eta) =  -6.67   S  
Iteration 8:   deviance = 5.4169e+02  eps = 1.33e-16  iters = 2    tol = 1.0e-07  min(eta) =  -6.67   S  
Iteration 9:   deviance = 5.4169e+02  eps = 1.33e-16  iters = 1    tol = 1.0e-09  min(eta) =  -6.67   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 9 iterations and 23 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =        718
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(10)   =      97.57
Deviance             =  541.6913193               Prob > chi2     =     0.0000
Log pseudolikelihood = -987.4325437               Pseudo R2       =     0.4336

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L3. |  -.0403804   .0490027    -0.82   0.410    -.1364238    .0556631
                  L2. |  -.0211633   .0454686    -0.47   0.642    -.1102801    .0679536
                  L1. |   .0858852   .0458456     1.87   0.061    -.0039705    .1757408
                  --. |   .1024632   .0516907     1.98   0.047     .0011513    .2037751
                  F1. |    .003772   .0518669     0.07   0.942    -.0978853    .1054292
                  F2. |  -.0583247   .0514763    -1.13   0.257    -.1592165    .0425671
                  F3. |  -.0051327   .0413032    -0.12   0.901    -.0860854      .07582
                      |
     deltaCommitSpend |
                  L1. |   .0000614   .0000349     1.76   0.079    -7.03e-06    .0001298
                      |
   AllNewTFs_ThisArea |
                  L1. |    .003703   .0135138     0.27   0.784    -.0227835    .0301895
                      |
  cumsumTFsInThisArea |
                  L1. |  -.0112752   .0015519    -7.27   0.000    -.0143168   -.0082335
                      |
                _cons |    1.95159   .0993978    19.63   0.000     1.756774    2.146406
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        26           0          26     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model12

. *Table
. esttab Model10 Model11 Model12 using TFPlacebos_19902021only.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> title("1990-2021 Spending Placebos") /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file TFPlacebos_19902021only.csv not found)
(output written to TFPlacebos_19902021only.csv)

. 
. *************************************************************************************************************************
. * Appendix D (Other Spend DVs)
. * Reload full data after dropping pre-1990 observations
. use "CRSg7frame.dta", clear

. xtset policyArea_num year
       panel variable:  policyArea_num (strongly balanced)
        time variable:  year, 1975 to 2021
                delta:  1 unit

. *drop 4 G7RG categories don't use
. drop if PolicyArea == "East-West (Russia)" | PolicyArea == "IFI/UN reform" | PolicyArea == "International cooperation" | PolicyArea == "Devel
> opment"
(188 observations deleted)

. 
. *App D Part i
. reghdfe deltaNONG7ONLYspend CommitNoIncreaseDummy ///
> , absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      1,296
Absorbing 2 HDFE groups                           F(   1,     28) =       4.07
Statistics robust to heteroskedasticity           Prob > F        =     0.0533
                                                  R-squared       =     0.1059
                                                  Adj R-squared   =     0.0509
                                                  Within R-sq.    =     0.0012
Number of clusters (policyArea_num) =         29  Root MSE        =   402.2785

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
  deltaNONG7ONLYspend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   34.54215   17.12307     2.02   0.053    -.5328745    69.61718
                _cons |  -9.156035   4.518589    -2.03   0.052    -18.41194    .0998745
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        46           0          46     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela10

. * a la Model 2 (Add Controls)
. reghdfe deltaNONG7ONLYspend CommitNoIncreaseDummy ///
> l.deltaNONG7ONLYspend l.AllNewTFs_ThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      1,268
Absorbing 2 HDFE groups                           F(   3,     28) =      26.85
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1569
                                                  Adj R-squared   =     0.1031
                                                  Within R-sq.    =     0.0583
Number of clusters (policyArea_num) =         29  Root MSE        =   395.3488

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
  deltaNONG7ONLYspend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   38.30353   17.56956     2.18   0.038     2.313913    74.29315
                      |
  deltaNONG7ONLYspend |
                  L1. |  -.2368646   .0669161    -3.54   0.001    -.3739361   -.0997931
                      |
   AllNewTFs_ThisArea |
                  L1. |  -7.400468    12.4962    -0.59   0.558    -32.99778    18.19685
                      |
                _cons |   2.119654   21.09506     0.10   0.921    -41.09162    45.33092
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        45           0          45     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela11

. * a la Model 3 (All Controls)
. reghdfe deltaNONG7ONLYspend CommitNoIncreaseDummy ///
> l.deltaNONG7ONLYspend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      1,268
Absorbing 2 HDFE groups                           F(   4,     28) =      20.30
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1569
                                                  Adj R-squared   =     0.1024
                                                  Within R-sq.    =     0.0583
Number of clusters (policyArea_num) =         29  Root MSE        =   395.5113

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
  deltaNONG7ONLYspend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   38.18652   17.37706     2.20   0.036     2.591233    73.78181
                      |
  deltaNONG7ONLYspend |
                  L1. |   -.236798   .0672591    -3.52   0.001     -.374572    -.099024
                      |
   AllNewTFs_ThisArea |
                  L1. |   -8.03031   17.33522    -0.46   0.647     -43.5399    27.47928
                      |
  cumsumTFsInThisArea |
                  L1. |   .0996675   .8770149     0.11   0.910    -1.696816    1.896151
                      |
                _cons |    .713274   13.72109     0.05   0.959     -27.3931    28.81965
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        45           0          45     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela12

. *Table
. esttab Modela10 Modela11 Modela12 using TabA4_nonG&SpendOnly.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file TabA4_nonG&SpendOnly.csv not found)
(output written to TabA4_nonG&SpendOnly.csv)

. 
. * App D Part ii (Delta bilateral spend only)
. * a la Model 1 (Correlation)
. reghdfe deltaSpendBilatOnly CommitNoIncreaseDummy ///
> , absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =        622
Absorbing 2 HDFE groups                           F(   1,     28) =       0.00
Statistics robust to heteroskedasticity           Prob > F        =     0.9905
                                                  R-squared       =     0.1222
                                                  Adj R-squared   =     0.0436
                                                  Within R-sq.    =     0.0000
Number of clusters (policyArea_num) =         29  Root MSE        =     5.8205

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
  deltaSpendBilatOnly |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |  -.0019381   .1614431    -0.01   0.991    -.3326393    .3287632
                _cons |   .4022476   .0550256     7.31   0.000     .2895327    .5149625
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        22           0          22     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela4

. * a la Model 2 (Add Controls)
. reghdfe deltaSpendBilatOnly CommitNoIncreaseDummy ///
> l.deltaSpendBilatOnly l.AllNewTFs_ThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =        566
Absorbing 2 HDFE groups                           F(   3,     28) =       1.94
Statistics robust to heteroskedasticity           Prob > F        =     0.1455
                                                  R-squared       =     0.4635
                                                  Adj R-squared   =     0.4103
                                                  Within R-sq.    =     0.3847
Number of clusters (policyArea_num) =         29  Root MSE        =     4.7906

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
  deltaSpendBilatOnly |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .2124908   .3700512     0.57   0.570    -.5455247    .9705063
                      |
  deltaSpendBilatOnly |
                  L1. |    1.47192   .6890349     2.14   0.042     .0604956    2.883344
                      |
   AllNewTFs_ThisArea |
                  L1. |   .3732539   .2371429     1.57   0.127    -.1125114    .8590192
                      |
                _cons |  -.7906183   .7765596    -1.02   0.317    -2.381329     .800092
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        20           0          20     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela5

. * a la Model 3 (All Controls)
. reghdfe deltaSpendBilatOnly CommitNoIncreaseDummy ///
> l.deltaSpendBilatOnly l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =        566
Absorbing 2 HDFE groups                           F(   4,     28) =       3.04
Statistics robust to heteroskedasticity           Prob > F        =     0.0337
                                                  R-squared       =     0.4635
                                                  Adj R-squared   =     0.4091
                                                  Within R-sq.    =     0.3847
Number of clusters (policyArea_num) =         29  Root MSE        =     4.7952

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
  deltaSpendBilatOnly |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .2123491   .3810608     0.56   0.582    -.5682186    .9929169
                      |
  deltaSpendBilatOnly |
                  L1. |   1.471939   .6882006     2.14   0.041     .0622239    2.881654
                      |
   AllNewTFs_ThisArea |
                  L1. |   .3728608    .257109     1.45   0.158    -.1538031    .8995246
                      |
  cumsumTFsInThisArea |
                  L1. |   .0001924   .0191264     0.01   0.992    -.0389862    .0393709
                      |
                _cons |   -.797523   .6961364    -1.15   0.262    -2.223494    .6284479
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        20           0          20     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela6

. *Table
. esttab Modela4 Modela5 Modela6 using TabA2_bilatSpendOnly.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file TabA2_bilatSpendOnly.csv not found)
(output written to TabA2_bilatSpendOnly.csv)

. 
. * Delta bilateral spend only, but lagged
. * a la Model 1 (Correlation)
. reghdfe deltaSpendBilatOnly l.CommitNoIncreaseDummy ///
> , absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =        621
Absorbing 2 HDFE groups                           F(   1,     28) =       3.52
Statistics robust to heteroskedasticity           Prob > F        =     0.0711
                                                  R-squared       =     0.1283
                                                  Adj R-squared   =     0.0502
                                                  Within R-sq.    =     0.0078
Number of clusters (policyArea_num) =         29  Root MSE        =     5.8084

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
  deltaSpendBilatOnly |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L1. |   1.110926   .5920942     1.88   0.071    -.1019235    2.323776
                      |
                _cons |   .0069262   .2154803     0.03   0.975    -.4344652    .4483176
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        22           0          22     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela7

. * a la Model 2 (Add Controls)
. reghdfe deltaSpendBilatOnly l.CommitNoIncreaseDummy ///
> l.deltaSpendBilatOnly l.AllNewTFs_ThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =        565
Absorbing 2 HDFE groups                           F(   3,     28) =       5.02
Statistics robust to heteroskedasticity           Prob > F        =     0.0065
                                                  R-squared       =     0.4682
                                                  Adj R-squared   =     0.4153
                                                  Within R-sq.    =     0.3906
Number of clusters (policyArea_num) =         29  Root MSE        =     4.7766

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
  deltaSpendBilatOnly |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L1. |   .9484225   .4384376     2.16   0.039     .0503239    1.846521
                      |
  deltaSpendBilatOnly |
                  L1. |   1.476386   .6852855     2.15   0.040     .0726422     2.88013
                      |
   AllNewTFs_ThisArea |
                  L1. |   .3451536   .2374355     1.45   0.157     -.141211    .8315181
                      |
                _cons |  -.9860147   .7584016    -1.30   0.204     -2.53953    .5675005
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        20           0          20     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela8

. * a la Model 3 (All Controls)
. reghdfe deltaSpendBilatOnly l.CommitNoIncreaseDummy ///
> l.deltaSpendBilatOnly l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =        565
Absorbing 2 HDFE groups                           F(   4,     28) =       4.71
Statistics robust to heteroskedasticity           Prob > F        =     0.0049
                                                  R-squared       =     0.4682
                                                  Adj R-squared   =     0.4142
                                                  Within R-sq.    =     0.3906
Number of clusters (policyArea_num) =         29  Root MSE        =     4.7812

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
  deltaSpendBilatOnly |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |
                  L1. |   .9495078   .4349766     2.18   0.038     .0584986    1.840517
                      |
  deltaSpendBilatOnly |
                  L1. |   1.476271   .6843748     2.16   0.040     .0743929    2.878149
                      |
   AllNewTFs_ThisArea |
                  L1. |   .3475729   .2577367     1.35   0.188    -.1803767    .8755225
                      |
  cumsumTFsInThisArea |
                  L1. |  -.0011915   .0185935    -0.06   0.949    -.0392786    .0368956
                      |
                _cons |  -.9432608   .7717827    -1.22   0.232    -2.524186    .6376645
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        20           0          20     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela9

. *Table
. esttab Modela7 Modela8 Modela9 using TabA3_bilatSpendOnly_LagG7.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file TabA3_bilatSpendOnly_LagG7.csv not found)
(output written to TabA3_bilatSpendOnly_LagG7.csv)

. 
. *************************************************************************************************************************
. *Appendix E (No lagged DV)
. *a la Model 2 (Add Controls)
. reghdfe deltaCommitSpend CommitNoIncreaseDummy ///
> l.AllNewTFs_ThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      1,296
Absorbing 2 HDFE groups                           F(   2,     28) =       2.47
Statistics robust to heteroskedasticity           Prob > F        =     0.1030
                                                  R-squared       =     0.1054
                                                  Adj R-squared   =     0.0496
                                                  Within R-sq.    =     0.0021
Number of clusters (policyArea_num) =         29  Root MSE        =   397.8642

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   30.82515   16.31249     1.89   0.069    -2.589468    64.23978
                      |
   AllNewTFs_ThisArea |
                  L1. |  -9.834753   10.89203    -0.90   0.374    -32.14606    12.47656
                      |
                _cons |   8.223682   19.22071     0.43   0.672    -31.14816    47.59553
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        46           0          46     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela13

. *a la Model 3 (All Controls)
. reghdfe deltaCommitSpend CommitNoIncreaseDummy ///
> l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      1,296
Absorbing 2 HDFE groups                           F(   3,     28) =       1.72
Statistics robust to heteroskedasticity           Prob > F        =     0.1862
                                                  R-squared       =     0.1055
                                                  Adj R-squared   =     0.0489
                                                  Within R-sq.    =     0.0022
Number of clusters (policyArea_num) =         29  Root MSE        =   398.0065

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   30.54613   16.38436     1.86   0.073    -3.015708    64.10798
                      |
   AllNewTFs_ThisArea |
                  L1. |   -11.3802   14.69247    -0.77   0.445    -41.47636    18.71596
                      |
  cumsumTFsInThisArea |
                  L1. |   .2392661   .7008691     0.34   0.735    -1.196399    1.674931
                      |
                _cons |   4.985649   13.15928     0.38   0.708    -21.96991    31.94121
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        46           0          46     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela14

. *a la Model 8 (Add Controls)
. ppmlhdfe AllNewTFs_ThisArea CommitNoIncreaseDummy ///
> l.deltaCommitSpend, absorb(policyArea_num year) cluster(policyArea_num)
(dropped 56 observations that are either singletons or separated by a fixed effect)
Iteration 1:   deviance = 1.1125e+03  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -3.18  P   
Iteration 2:   deviance = 9.4707e+02  eps = 1.75e-01  iters = 3    tol = 1.0e-04  min(eta) =  -4.86      
Iteration 3:   deviance = 9.1803e+02  eps = 3.16e-02  iters = 2    tol = 1.0e-04  min(eta) =  -6.39      
Iteration 4:   deviance = 9.1424e+02  eps = 4.14e-03  iters = 2    tol = 1.0e-04  min(eta) =  -7.41      
Iteration 5:   deviance = 9.1406e+02  eps = 2.02e-04  iters = 2    tol = 1.0e-04  min(eta) =  -7.79      
Iteration 6:   deviance = 9.1406e+02  eps = 1.42e-06  iters = 2    tol = 1.0e-04  min(eta) =  -7.83      
Iteration 7:   deviance = 9.1406e+02  eps = 2.16e-10  iters = 2    tol = 1.0e-05  min(eta) =  -7.83   S  
Iteration 8:   deviance = 9.1406e+02  eps = 0.00e+00  iters = 2    tol = 1.0e-07  min(eta) =  -7.83   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 8 iterations and 19 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =      1,212
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(2)    =       4.75
Deviance             =  914.0562982               Prob > chi2     =     0.0932
Log pseudolikelihood = -1414.254068               Pseudo R2       =     0.4799

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .1073612   .0536874     2.00   0.046      .002136    .2125865
                      |
     deltaCommitSpend |
                  L1. |   .0000688   .0000436     1.58   0.115    -.0000167    .0001542
                      |
                _cons |   1.145193   .0174284    65.71   0.000     1.111033    1.179352
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        43           0          43     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela15

. *a la Model 9 (All Controls)
. ppmlhdfe AllNewTFs_ThisArea CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(dropped 56 observations that are either singletons or separated by a fixed effect)
Iteration 1:   deviance = 1.1101e+03  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -3.19  P   
Iteration 2:   deviance = 8.9019e+02  eps = 2.47e-01  iters = 3    tol = 1.0e-04  min(eta) =  -5.28      
Iteration 3:   deviance = 8.4902e+02  eps = 4.85e-02  iters = 3    tol = 1.0e-04  min(eta) =  -7.08      
Iteration 4:   deviance = 8.4430e+02  eps = 5.58e-03  iters = 3    tol = 1.0e-04  min(eta) =  -8.19      
Iteration 5:   deviance = 8.4407e+02  eps = 2.81e-04  iters = 3    tol = 1.0e-04  min(eta) =  -8.60      
Iteration 6:   deviance = 8.4407e+02  eps = 2.25e-06  iters = 2    tol = 1.0e-04  min(eta) =  -8.65      
Iteration 7:   deviance = 8.4407e+02  eps = 4.42e-10  iters = 2    tol = 1.0e-05  min(eta) =  -8.65   S  
Iteration 8:   deviance = 8.4407e+02  eps = 1.56e-16  iters = 2    tol = 1.0e-07  min(eta) =  -8.65   S  
Iteration 9:   deviance = 8.4407e+02  eps = 1.56e-16  iters = 1    tol = 1.0e-09  min(eta) =  -8.65   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 9 iterations and 23 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =      1,212
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(3)    =     106.06
Deviance             =   844.065303               Prob > chi2     =     0.0000
Log pseudolikelihood =  -1379.25857               Pseudo R2       =     0.4928

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .1069186   .0488892     2.19   0.029     .0110974    .2027398
                      |
     deltaCommitSpend |
                  L1. |    .000074   .0000399     1.85   0.064    -4.19e-06    .0001522
                      |
  cumsumTFsInThisArea |
                  L1. |  -.0132524   .0013371    -9.91   0.000    -.0158731   -.0106317
                      |
                _cons |   1.928452   .0787158    24.50   0.000     1.774172    2.082732
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        43           0          43     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Modela16

. *Table
. esttab Modela13 Modela14 Modela15 Modela16 using TabA5_NoLaggedDVmodels.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file TabA5_NoLaggedDVmodels.csv not found)
(output written to TabA5_NoLaggedDVmodels.csv)

. 
. *************************************************************************************************************************
. *Appendix F
. *Appendix Fi (Change in Commitment #)
. *Spend and TF Basic (Models 3 and 9)
. regress deltaCommitSpend CommitNoIncreaseDummy l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea deltaTotalAnnCommits i.policyAre
> a_num, cluster(policyArea_num)

Linear regression                               Number of obs     =      1,268
                                                F(4, 28)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0523
                                                Root MSE          =        407

                                   (Std. Err. adjusted for 29 clusters in policyArea_num)
-----------------------------------------------------------------------------------------
                        |               Robust
       deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
  CommitNoIncreaseDummy |   57.61731   21.73271     2.65   0.013     13.09987    102.1348
                        |
       deltaCommitSpend |
                    L1. |  -.2188255   .0594367    -3.68   0.001     -.340576    -.097075
                        |
     AllNewTFs_ThisArea |
                    L1. |  -6.067038   13.00078    -0.47   0.644    -32.69794    20.56386
                        |
    cumsumTFsInThisArea |
                    L1. |   -.624364   .4752768    -1.31   0.200    -1.597924    .3491964
                        |
   deltaTotalAnnCommits |  -.2098897   .1203373    -1.74   0.092    -.4563895    .0366101
                        |
         policyArea_num |
        Climate change  |  -31.98947   13.13723    -2.44   0.022    -58.89987   -5.079082
   Conflict prevention  |  -28.03763   16.79845    -1.67   0.106     -62.4477    6.372426
  Crime and Corruption  |   -10.0408   3.417337    -2.94   0.007    -17.04089   -3.040698
             Democracy  |   3.810699   1.449077     2.63   0.014     .8423986    6.778999
                 Drugs  |  -50.69667   27.92554    -1.82   0.080    -107.8995    6.506191
             Education  |  -47.16801   25.22046    -1.87   0.072    -98.82978    4.493767
                Energy  |  -48.22492   23.28929    -2.07   0.048    -95.93086   -.5189738
           Environment  |  -31.98947   13.13723    -2.44   0.022    -58.89987   -5.079082
  Financial regulation  |   -45.0268   23.08389    -1.95   0.061    -92.31202    2.258413
    Food & Agriculture  |  -39.85457   20.05981    -1.99   0.057    -80.94522    1.236084
                Gender  |   6.401923   2.414746     2.65   0.013     1.455541     11.3483
       Good governance  |   7.682308   2.897695     2.65   0.013      1.74665    13.61797
                Health  |  -43.49757   20.51496    -2.12   0.043    -85.52056   -1.474579
          Human rights  |  -1.280385   .4829491    -2.65   0.013    -2.269662    -.291109
   ICT/Digital economy  |  -71.58189   27.51704    -2.60   0.015     -127.948   -15.21578
        Infrastructure  |  -23.67558   12.06201    -1.96   0.060    -48.38348    1.032324
   Labour & Employment  |  -48.17121   21.42301    -2.25   0.033    -92.05426   -4.288158
  Macroeconomic policy  |    -16.645   6.278338    -2.65   0.013    -29.50559   -3.784408
  Microeconomic policy  |  -40.86048   22.57055    -1.81   0.081    -87.09416    5.373198
Migration and refugees  |   8.962693   3.380644     2.65   0.013     2.037758    15.88763
      Nonproliferation  |  -61.49262   30.39848    -2.02   0.053    -123.7611    .7758346
        Nuclear safety  |  -52.52993   28.15183    -1.87   0.073    -110.1963    5.136471
      Peace & security  |  -33.15917   17.84757    -1.86   0.074    -69.71826    3.399909
     Regional security  |  -44.68264   20.67866    -2.16   0.039    -87.04096   -2.324312
         Social policy  |  -37.77099   18.49435    -2.04   0.051    -75.65495    .1129747
             Terrorism  |  -52.36494   22.83228    -2.29   0.030    -99.13476   -5.595134
                 Trade  |  -54.97849   26.59773    -2.07   0.048    -109.4615   -.4955167
          Transparency  |   12.80385   4.829491     2.65   0.013     2.911083    22.69661
                        |
                  _cons |   41.48434    26.3521     1.57   0.127    -12.49549    95.46416
-----------------------------------------------------------------------------------------

. estimates store Modela17

. regress AllNewTFs_ThisArea CommitNoIncreaseDummy l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea deltaTotalAnnCommits i.policyA
> rea_num, cluster(policyArea_num)

Linear regression                               Number of obs     =      1,268
                                                F(4, 28)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.5379
                                                Root MSE          =     1.5825

                                   (Std. Err. adjusted for 29 clusters in policyArea_num)
-----------------------------------------------------------------------------------------
                        |               Robust
     AllNewTFs_ThisArea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
  CommitNoIncreaseDummy |   .4299335   .1174703     3.66   0.001     .1893065    .6705606
                        |
       deltaCommitSpend |
                    L1. |   .0001637   .0000998     1.64   0.112    -.0000408    .0003682
                        |
     AllNewTFs_ThisArea |
                    L1. |   .3008257   .0309404     9.72   0.000     .2374471    .3642043
                        |
    cumsumTFsInThisArea |
                    L1. |   .0196836   .0010323    19.07   0.000      .017569    .0217982
                        |
   deltaTotalAnnCommits |  -.0028766   .0006277    -4.58   0.000    -.0041624   -.0015908
                        |
         policyArea_num |
        Climate change  |  -.3766109    .031511   -11.95   0.000    -.4411582   -.3120636
   Conflict prevention  |  -.7222952   .0600207   -12.03   0.000     -.845242   -.5993484
  Crime and Corruption  |  -.2037374   .0118478   -17.20   0.000    -.2280065   -.1794684
             Democracy  |   .0286635   .0078313     3.66   0.001     .0126217    .0447053
                 Drugs  |  -1.235049   .0779786   -15.84   0.000    -1.394781   -1.075317
             Education  |  -1.111277   .0710567   -15.64   0.000     -1.25683   -.9657238
                Energy  |  -.9622155    .056331   -17.08   0.000    -1.077604   -.8468265
           Environment  |  -.3766109    .031511   -11.95   0.000    -.4411582   -.3120636
  Financial regulation  |  -.9690258    .064077   -15.12   0.000    -1.100282   -.8377701
    Food & Agriculture  |  -.8583929   .0532446   -16.12   0.000    -.9674595   -.7493262
                Gender  |   .0477704   .0130523     3.66   0.001     .0210341    .0745067
       Good governance  |   .0573245   .0156627     3.66   0.001     .0252409    .0894081
                Health  |  -.7989384    .052307   -15.27   0.000    -.9060844   -.6917924
          Human rights  |  -.0095541   .0026105    -3.66   0.001    -.0149013   -.0042068
   ICT/Digital economy  |  -1.292004   .0659912   -19.58   0.000    -1.427181   -1.156827
        Infrastructure  |  -.4214167   .0403062   -10.46   0.000    -.5039801   -.3388532
   Labour & Employment  |  -.8013559   .0522734   -15.33   0.000     -.908433   -.6942788
  Macroeconomic policy  |   -.124203   .0339359    -3.66   0.001    -.1937175   -.0546886
  Microeconomic policy  |  -1.002968   .0656086   -15.29   0.000    -1.137362   -.8685753
Migration and refugees  |   .0668786   .0182732     3.66   0.001     .0294477    .1043094
      Nonproliferation  |  -1.326619    .073907   -17.95   0.000     -1.47801   -1.175227
        Nuclear safety  |   -1.25974   .0756917   -16.64   0.000    -1.414788   -1.104693
      Peace & security  |  -.7605116   .0551719   -13.78   0.000     -.873526   -.6474971
     Regional security  |  -.8464983   .0508379   -16.65   0.000    -.9506349   -.7423616
         Social policy  |  -.7153663   .0552592   -12.95   0.000    -.8285597   -.6021729
             Terrorism  |  -.9038227   .0537726   -16.81   0.000    -1.013971   -.7936745
                 Trade  |  -1.146574   .0634372   -18.07   0.000    -1.276519   -1.016628
          Transparency  |   .0955408   .0261045     3.66   0.001     .0420681    .1490135
                        |
                  _cons |   1.206608   .0912377    13.22   0.000     1.019716      1.3935
-----------------------------------------------------------------------------------------

. estimates store Modela18

. *Spend and TF Placebos (Models 6 and 12)
. regress deltaCommitSpend l3.CommitNoIncreaseDummy l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDu
> mmy f2.CommitNoIncreaseDummy f3.CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea deltaTotalAnnCommits i.policyArea_num, cluster(policyArea_num)

Linear regression                               Number of obs     =      1,110
                                                F(10, 28)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0693
                                                Root MSE          =     337.59

                                   (Std. Err. adjusted for 29 clusters in policyArea_num)
-----------------------------------------------------------------------------------------
                        |               Robust
       deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
  CommitNoIncreaseDummy |
                    L3. |   12.78336    26.4677     0.48   0.633    -41.43327          67
                    L2. |  -.0289047   21.54016    -0.00   0.999    -44.15192    44.09411
                    L1. |   8.982549    28.6169     0.31   0.756    -49.63651    67.60161
                    --. |   23.68274   12.45604     1.90   0.068      -1.8323    49.19778
                    F1. |    6.94835   12.18769     0.57   0.573    -18.01701    31.91371
                    F2. |   28.89944   29.08782     0.99   0.329    -30.68425    88.48313
                    F3. |   9.023912   6.338185     1.42   0.166    -3.959271    22.00709
                        |
       deltaCommitSpend |
                    L1. |  -.2469173   .0663142    -3.72   0.001    -.3827558   -.1110788
                        |
     AllNewTFs_ThisArea |
                    L1. |  -7.663924   5.748666    -1.33   0.193    -19.43953    4.111684
                        |
    cumsumTFsInThisArea |
                    L1. |   .6676166   .2838889     2.35   0.026     .0860966    1.249137
                        |
   deltaTotalAnnCommits |  -.1542437   .1534934    -1.00   0.324    -.4686607    .1601732
                        |
         policyArea_num |
        Climate change  |  -6.265312   9.386508    -0.67   0.510     -25.4927    12.96208
   Conflict prevention  |    25.0663   10.89338     2.30   0.029     2.752223    47.38038
  Crime and Corruption  |   6.151153   4.034901     1.52   0.139    -2.113966    14.41627
             Democracy  |   96.52512   3.939135    24.50   0.000     88.45617    104.5941
                 Drugs  |    9.72612   11.16841     0.87   0.391    -13.15132    32.60356
             Education  |    10.7828   9.974612     1.08   0.289    -9.649271    31.21486
                Energy  |  -2.932221   15.63562    -0.19   0.853    -34.96033    29.09589
           Environment  |  -6.265312   9.386508    -0.67   0.510     -25.4927    12.96208
  Financial regulation  |   82.94419   11.37635     7.29   0.000      59.6408    106.2476
    Food & Agriculture  |   7.258266   7.992205     0.91   0.372    -9.113025    23.62956
                Gender  |   68.75404   8.674134     7.93   0.000     50.98588     86.5222
       Good governance  |   11.92488   8.372534     1.42   0.165    -5.225481    29.07523
                Health  |   5.434417   9.676146     0.56   0.579    -14.38627     25.2551
          Human rights  |   35.94574   2.325594    15.46   0.000     31.18198    40.70951
   ICT/Digital economy  |   11.71642   12.37727     0.95   0.352    -13.63727    37.07011
        Infrastructure  |   17.82048   8.428262     2.11   0.044     .5559633    35.08499
   Labour & Employment  |   43.78011   15.34933     2.85   0.008     12.33843     75.2218
  Macroeconomic policy  |  -23.16607   13.30029    -1.74   0.093    -50.41047    4.078331
  Microeconomic policy  |   11.52942   9.050526     1.27   0.213    -7.009747    30.06858
Migration and refugees  |   29.42338   10.06701     2.92   0.007     8.802046    50.04472
      Nonproliferation  |  -8.686591   17.25976    -0.50   0.619    -44.04162    26.66843
        Nuclear safety  |   5.402308   11.72346     0.46   0.648    -18.61211    29.41673
      Peace & security  |   16.35674   8.286238     1.97   0.058     -.616851    33.33033
     Regional security  |   .1557058   10.47334     0.01   0.988    -21.29795    21.60936
         Social policy  |     167.52   11.80058    14.20   0.000     143.3476    191.6924
             Terrorism  |  -12.26446   16.02299    -0.77   0.450    -45.08607    20.55714
                 Trade  |  -8.418536   15.01472    -0.56   0.579     -39.1748    22.33772
          Transparency  |   20.63444   13.31405     1.55   0.132    -6.638161    47.90704
                        |
                  _cons |  -28.98383    14.5851    -1.99   0.057    -58.86006    .8923957
-----------------------------------------------------------------------------------------

. estimates store Modela19

. regress AllNewTFs_ThisArea l3.CommitNoIncreaseDummy l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncrease
> Dummy f2.CommitNoIncreaseDummy f3.CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea deltaTotalAnnCommits i.policyArea_num, cluster(policyArea_num)

Linear regression                               Number of obs     =      1,110
                                                F(10, 28)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6089
                                                Root MSE          =      1.469

                                   (Std. Err. adjusted for 29 clusters in policyArea_num)
-----------------------------------------------------------------------------------------
                        |               Robust
     AllNewTFs_ThisArea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
  CommitNoIncreaseDummy |
                    L3. |    .086414   .1147651     0.75   0.458    -.1486716    .3214996
                    L2. |   .1433697   .1000749     1.43   0.163    -.0616244    .3483638
                    L1. |   .0937485   .1016374     0.92   0.364    -.1144463    .3019434
                    --. |   .3168349   .1233779     2.57   0.016     .0641067    .5695632
                    F1. |   .0486361   .0965261     0.50   0.618    -.1490886    .2463608
                    F2. |   .1038661   .1092144     0.95   0.350    -.1198494    .3275816
                    F3. |   .2130226   .1238702     1.72   0.097     -.040714    .4667593
                        |
       deltaCommitSpend |
                    L1. |   .0001148   .0000912     1.26   0.219    -.0000721    .0003017
                        |
     AllNewTFs_ThisArea |
                    L1. |   .1693126   .0426622     3.97   0.000     .0819231    .2567021
                        |
    cumsumTFsInThisArea |
                    L1. |   .0341631   .0018042    18.94   0.000     .0304673    .0378588
                        |
   deltaTotalAnnCommits |  -.0021261   .0005539    -3.84   0.001    -.0032606   -.0009915
                        |
         policyArea_num |
        Climate change  |   -.486027    .068761    -7.07   0.000    -.6268775   -.3451764
   Conflict prevention  |  -.6085932   .0607549   -10.02   0.000    -.7330439   -.4841425
  Crime and Corruption  |  -.2633376   .0419667    -6.27   0.000    -.3493024   -.1773728
             Democracy  |   .0768604   .0267928     2.87   0.008     .0219779     .131743
                 Drugs  |  -1.237752   .0815155   -15.18   0.000    -1.404729   -1.070775
             Education  |  -1.081296   .0726733   -14.88   0.000    -1.230161   -.9324319
                Energy  |  -1.007287   .1010929    -9.96   0.000    -1.214367   -.8002076
           Environment  |   -.486027    .068761    -7.07   0.000    -.6268775   -.3451764
  Financial regulation  |  -.9221013   .0672254   -13.72   0.000    -1.059806   -.7843963
    Food & Agriculture  |  -.8586627    .062305   -13.78   0.000    -.9862888   -.7310366
                Gender  |   .1394913   .0457362     3.05   0.005     .0458049    .2331777
       Good governance  |   .1336414   .0433315     3.08   0.005     .0448809     .222402
                Health  |  -.8274519   .0712266   -11.62   0.000    -.9733529   -.6815508
          Human rights  |  -.0283021   .0119066    -2.38   0.025    -.0526916   -.0039126
   ICT/Digital economy  |  -1.311544   .1034618   -12.68   0.000    -1.523476   -1.099612
        Infrastructure  |  -.3224048   .0361703    -8.91   0.000    -.3964964   -.2483133
   Labour & Employment  |  -.9089004   .0837961   -10.85   0.000    -1.080549   -.7372519
  Macroeconomic policy  |  -.2546181   .0846774    -3.01   0.006    -.4280719   -.0811643
  Microeconomic policy  |  -.9809372   .0635157   -15.44   0.000    -1.111043   -.8508313
Migration and refugees  |   .1675786   .0542312     3.09   0.004      .056491    .2786662
      Nonproliferation  |  -1.425782   .1239792   -11.50   0.000    -1.679742   -1.171822
        Nuclear safety  |  -1.267412   .0870332   -14.56   0.000    -1.445691   -1.089133
      Peace & security  |  -.7070221   .0513798   -13.76   0.000    -.8122687   -.6017754
     Regional security  |  -.8860104   .0814022   -10.88   0.000    -1.052755   -.7192656
         Social policy  |  -.7416355   .0522323   -14.20   0.000    -.8486285   -.6346424
             Terrorism  |  -1.028358   .1175694    -8.75   0.000    -1.269188   -.7875285
                 Trade  |  -1.210716   .1090865   -11.10   0.000     -1.43417    -.987263
          Transparency  |   .2320703   .0754864     3.07   0.005     .0774435    .3866971
                        |
                  _cons |   1.035176   .0875107    11.83   0.000     .8559182    1.214433
-----------------------------------------------------------------------------------------

. estimates store Modela20

. *Table
. esttab Modela17 Modela18 Modela19 Modela20 using TabA11_ChangeTotG7commits.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> note("Unit fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file TabA11_ChangeTotG7commits.csv not found)
(output written to TabA11_ChangeTotG7commits.csv)

. *Appendix Fii (Control for # of dev commits)
. regress deltaCommitSpend CommitNoIncreaseDummy l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea deltaDevCommitNumberThisYr i.pol
> icyArea_num, cluster(policyArea_num)

Linear regression                               Number of obs     =      1,268
                                                F(4, 28)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0511
                                                Root MSE          =     407.25

                                      (Std. Err. adjusted for 29 clusters in policyArea_num)
--------------------------------------------------------------------------------------------
                           |               Robust
          deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
     CommitNoIncreaseDummy |   51.28882   21.55636     2.38   0.024     7.132613    95.44502
                           |
          deltaCommitSpend |
                       L1. |  -.2114924   .0591647    -3.57   0.001    -.3326857   -.0902991
                           |
        AllNewTFs_ThisArea |
                       L1. |  -3.550057   11.89447    -0.30   0.768    -27.91478    20.81467
                           |
       cumsumTFsInThisArea |
                       L1. |  -.7692087   .4297309    -1.79   0.084    -1.649472     .111055
                           |
deltaDevCommitNumberThisYr |  -.9719401   .8022397    -1.21   0.236    -2.615254    .6713735
                           |
            policyArea_num |
           Climate change  |  -31.72806   13.26124    -2.39   0.024    -58.89248   -4.563649
      Conflict prevention  |  -29.32395    17.1923    -1.71   0.099    -64.54078    5.892876
     Crime and Corruption  |  -9.380675   3.316023    -2.83   0.009    -16.17324   -2.588111
                Democracy  |   3.388857   1.437329     2.36   0.026     .4446231    6.333092
                    Drugs  |  -50.42752   27.92254    -1.81   0.082    -107.6242    6.769202
                Education  |  -47.21398   25.33132    -1.86   0.073    -99.10283    4.674875
                   Energy  |  -47.74057   23.43398    -2.04   0.051     -95.7429    .2617687
              Environment  |  -31.72806   13.26124    -2.39   0.024    -58.89248   -4.563649
     Financial regulation  |  -45.18489    23.2546    -1.94   0.062    -92.81977    2.449989
       Food & Agriculture  |  -39.68164   20.13295    -1.97   0.059    -80.92211     1.55884
                   Gender  |   5.698757   2.395151     2.38   0.024     .7925117      10.605
          Good governance  |   6.838509   2.874181     2.38   0.024     .9510151      12.726
                   Health  |  -43.29443   20.63672    -2.10   0.045    -85.56683   -1.022034
             Human rights  |  -1.139752   .4790302    -2.38   0.024    -2.121001   -.1585032
      ICT/Digital economy  |  -73.05447   28.66699    -2.55   0.017    -131.7761    -14.3328
           Infrastructure  |  -24.81559   12.45146    -1.99   0.056    -50.32126    .6900799
      Labour & Employment  |   -47.7393    21.5463    -2.22   0.035    -91.87489   -3.603715
     Macroeconomic policy  |  -14.81677   6.227393    -2.38   0.024    -27.57301   -2.060532
     Microeconomic policy  |  -41.01458   22.67752    -1.81   0.081    -87.46738    5.438211
   Migration and refugees  |    7.97826   3.353212     2.38   0.024     1.109518      14.847
         Nonproliferation  |  -59.96342   30.17494    -1.99   0.057     -121.774    1.847157
           Nuclear safety  |  -51.98516   28.09671    -1.85   0.075    -109.5387    5.568342
         Peace & security  |  -33.88296   18.12617    -1.87   0.072    -71.01272    3.246809
        Regional security  |  -44.14072    20.7272    -2.13   0.042    -86.59847   -1.682966
            Social policy  |  -38.60465   18.84304    -2.05   0.050    -77.20286   -.0064426
                Terrorism  |  -50.97923   22.75175    -2.24   0.033    -97.58407   -4.374385
                    Trade  |  -53.41904   26.36948    -2.03   0.052    -107.4345    .5963983
             Transparency  |   11.39751   4.790302     2.38   0.024     1.585025       21.21
                           |
                     _cons |   40.82452   26.10232     1.56   0.129    -12.64366    94.29271
--------------------------------------------------------------------------------------------

. estimates store Modela21

. regress AllNewTFs_ThisArea CommitNoIncreaseDummy l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea deltaDevCommitNumberThisYr i.p
> olicyArea_num, cluster(policyArea_num)

Linear regression                               Number of obs     =      1,268
                                                F(4, 28)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.5453
                                                Root MSE          =     1.5696

                                      (Std. Err. adjusted for 29 clusters in policyArea_num)
--------------------------------------------------------------------------------------------
                           |               Robust
        AllNewTFs_ThisArea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
     CommitNoIncreaseDummy |   .3939308   .1172496     3.36   0.002     .1537558    .6341058
                           |
          deltaCommitSpend |
                       L1. |   .0002449   .0001069     2.29   0.030     .0000259    .0004638
                           |
        AllNewTFs_ThisArea |
                       L1. |    .342376   .0260753    13.13   0.000     .2889631    .3957889
                           |
       cumsumTFsInThisArea |
                       L1. |   .0171545   .0010514    16.32   0.000     .0150008    .0193081
                           |
deltaDevCommitNumberThisYr |  -.0236158   .0046975    -5.03   0.000    -.0332381   -.0139935
                           |
            policyArea_num |
           Climate change  |  -.3836818   .0297856   -12.88   0.000    -.4446949   -.3226687
      Conflict prevention  |   -.735452   .0554237   -13.27   0.000    -.8489823   -.6219216
     Crime and Corruption  |  -.1980134   .0118562   -16.70   0.000    -.2222999    -.173727
                Democracy  |    .026264   .0078166     3.36   0.002     .0102523    .0422757
                    Drugs  |  -1.233538   .0696424   -17.71   0.000    -1.376194   -1.090882
                Education  |  -1.114575   .0638757   -17.45   0.000    -1.245418   -.9837312
                   Energy  |  -.9700388   .0519278   -18.68   0.000    -1.076408   -.8636696
              Environment  |  -.3836818   .0297856   -12.88   0.000    -.4446949   -.3226687
     Financial regulation  |  -.9752972   .0578924   -16.85   0.000    -1.093884     -.85671
       Food & Agriculture  |  -.8611294   .0479084   -17.97   0.000    -.9592653   -.7629935
                   Gender  |   .0437701   .0130277     3.36   0.002      .017084    .0704562
          Good governance  |   .0525241   .0156333     3.36   0.002     .0205008    .0845474
                   Health  |  -.8044586   .0475031   -16.93   0.000    -.9017644   -.7071529
             Human rights  |   -.008754   .0026055    -3.36   0.002    -.0140912   -.0034168
      ICT/Digital economy  |  -1.346448   .0656172   -20.52   0.000    -1.480859   -1.212037
           Infrastructure  |  -.4370419   .0376487   -11.61   0.000    -.5141619    -.359922
      Labour & Employment  |  -.8078481   .0480401   -16.82   0.000    -.9062537   -.7094425
     Macroeconomic policy  |  -.1138022   .0338721    -3.36   0.002    -.1831861   -.0444184
     Microeconomic policy  |  -1.005806   .0590119   -17.04   0.000    -1.126686   -.8849253
   Migration and refugees  |   .0612781   .0182388     3.36   0.002     .0239176    .0986387
         Nonproliferation  |  -1.317898   .0658796   -20.00   0.000    -1.452847    -1.18295
           Nuclear safety  |   -1.25662   .0674314   -18.64   0.000    -1.394747   -1.118493
         Peace & security  |   -.770468   .0503835   -15.29   0.000     -.873674   -.6672621
        Regional security  |  -.8492542   .0461832   -18.39   0.000    -.9438563   -.7546521
            Social policy  |  -.7290577   .0507393   -14.37   0.000    -.8329923    -.625123
                Terrorism  |  -.9017783   .0497342   -18.13   0.000    -1.003654   -.7999025
                    Trade  |  -1.137808   .0567545   -20.05   0.000    -1.254065   -1.021552
             Transparency  |   .0875402   .0260555     3.36   0.002      .034168    .1409124
                           |
                     _cons |   1.192835    .080564    14.81   0.000     1.027807    1.357863
--------------------------------------------------------------------------------------------

. estimates store Modela22

. *Spend and TF Placebos (Models 6 and 12)
. regress deltaCommitSpend l3.CommitNoIncreaseDummy l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncreaseDu
> mmy f2.CommitNoIncreaseDummy f3.CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea deltaDevCommitNumberThisYr i.policyArea_num, cluster(policyArea_num)

Linear regression                               Number of obs     =      1,110
                                                F(10, 28)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0703
                                                Root MSE          =     337.41

                                      (Std. Err. adjusted for 29 clusters in policyArea_num)
--------------------------------------------------------------------------------------------
                           |               Robust
          deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
     CommitNoIncreaseDummy |
                       L3. |   12.58632   25.95975     0.48   0.632    -40.58982    65.76246
                       L2. |  -.8088391   21.05574    -0.04   0.970    -43.93957    42.32189
                       L1. |   8.516417   29.03975     0.29   0.771    -50.96882    68.00165
                       --. |   22.13352    13.2399     1.67   0.106    -4.987179    49.25422
                       F1. |   7.115094   12.17835     0.58   0.564    -17.83112    32.06131
                       F2. |   27.84982   29.02483     0.96   0.346    -31.60485    87.30449
                       F3. |   8.374133   6.196388     1.35   0.187    -4.318593    21.06686
                           |
          deltaCommitSpend |
                       L1. |  -.2449328   .0663205    -3.69   0.001    -.3807842   -.1090815
                           |
        AllNewTFs_ThisArea |
                       L1. |  -6.196432   5.168564    -1.20   0.241    -16.78375    4.390891
                           |
       cumsumTFsInThisArea |
                       L1. |   .5962577   .2745138     2.17   0.038     .0339416    1.158574
                           |
deltaDevCommitNumberThisYr |  -1.097182   .9583282    -1.14   0.262    -3.060228    .8658646
                           |
            policyArea_num |
           Climate change  |  -5.465702   8.867175    -0.62   0.543    -23.62929    12.69788
      Conflict prevention  |   25.07203   11.05685     2.27   0.031     2.423098    47.72097
     Crime and Corruption  |   6.070603   3.848136     1.58   0.126    -1.811947    13.95315
                Democracy  |   96.01898   3.653442    26.28   0.000     88.53524    103.5027
                    Drugs  |   10.99404    10.6521     1.03   0.311     -10.8258    32.81387
                Education  |   11.78495   9.654419     1.22   0.232    -7.991226    31.56113
                   Energy  |  -.5845494   14.10617    -0.04   0.967    -29.47973    28.31063
              Environment  |  -5.465702   8.867175    -0.62   0.543    -23.62929    12.69788
     Financial regulation  |   83.72851    10.9328     7.66   0.000     61.33367    106.1233
       Food & Agriculture  |   8.202336   7.542467     1.09   0.286    -7.247707    23.65238
                   Gender  |   68.06888   8.310704     8.19   0.000     51.04517    85.09258
          Good governance  |    11.3299   8.093905     1.40   0.173    -5.249709    27.90952
                   Health  |   6.438853   9.117611     0.71   0.486    -12.23773    25.11543
             Human rights  |   35.98892   2.332415    15.43   0.000     31.21119    40.76666
      ICT/Digital economy  |   12.67231   11.32446     1.12   0.273     -10.5248    35.86942
           Infrastructure  |   17.50712   8.463539     2.07   0.048     .1703461    34.84389
      Labour & Employment  |   44.88194   14.62856     3.07   0.005     14.91669    74.84719
     Macroeconomic policy  |  -22.02316   12.78542    -1.72   0.096    -48.21291    4.166595
     Microeconomic policy  |   12.35413   8.842091     1.40   0.173    -5.758076    30.46633
   Migration and refugees  |   28.67747   9.708322     2.95   0.006      8.79087    48.56406
         Nonproliferation  |  -6.522639   15.85724    -0.41   0.684    -39.00472    25.95944
           Nuclear safety  |   6.866342   10.99219     0.62   0.537    -15.65014    29.38283
         Peace & security  |   16.81004   8.322657     2.02   0.053    -.2381463    33.85823
        Regional security  |   1.393461   9.691628     0.14   0.887    -18.45894    21.24586
            Social policy  |   167.8184   11.55781    14.52   0.000     144.1433    191.4935
                Terrorism  |  -10.37137   14.82636    -0.70   0.490    -40.74178    19.99904
                    Trade  |  -6.498183    13.7727    -0.47   0.641    -34.71028    21.71391
             Transparency  |   19.59189   12.83834     1.53   0.138    -6.706261    45.89005
                           |
                     _cons |  -29.55582   14.81779    -1.99   0.056    -59.90868    .7970403
--------------------------------------------------------------------------------------------

. estimates store Modela23

. regress AllNewTFs_ThisArea l3.CommitNoIncreaseDummy l2.CommitNoIncreaseDummy l.CommitNoIncreaseDummy CommitNoIncreaseDummy f.CommitNoIncrease
> Dummy f2.CommitNoIncreaseDummy f3.CommitNoIncreaseDummy ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea deltaDevCommitNumberThisYr i.policyArea_num, cluster(policyArea_num)

Linear regression                               Number of obs     =      1,110
                                                F(10, 28)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6214
                                                Root MSE          =     1.4453

                                      (Std. Err. adjusted for 29 clusters in policyArea_num)
--------------------------------------------------------------------------------------------
                           |               Robust
        AllNewTFs_ThisArea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
     CommitNoIncreaseDummy |
                       L3. |   .0730713   .1103039     0.66   0.513     -.152876    .2990187
                       L2. |   .1290067   .0957127     1.35   0.189    -.0670518    .3250652
                       L1. |   .0773067   .0999392     0.77   0.446    -.1274095    .2820229
                       --. |   .3139399    .119121     2.64   0.014     .0699317    .5579481
                       F1. |   .0466032   .0953099     0.49   0.629    -.1486302    .2418365
                       F2. |    .092486   .1069268     0.86   0.394    -.1265436    .3115156
                       F3. |   .2048312    .119107     1.72   0.097    -.0391485    .4488109
                           |
          deltaCommitSpend |
                       L1. |   .0001441   .0000953     1.51   0.142    -.0000511    .0003393
                           |
        AllNewTFs_ThisArea |
                       L1. |   .1977958   .0388958     5.09   0.000     .1181214    .2774701
                           |
       cumsumTFsInThisArea |
                       L1. |   .0328732    .001716    19.16   0.000     .0293581    .0363884
                           |
deltaDevCommitNumberThisYr |  -.0212181   .0040639    -5.22   0.000    -.0295427   -.0128935
                           |
            policyArea_num |
           Climate change  |  -.4711713   .0644302    -7.31   0.000    -.6031505   -.3391921
      Conflict prevention  |  -.6012631   .0568771   -10.57   0.000    -.7177706   -.4847557
     Crime and Corruption  |  -.2599273   .0401684    -6.47   0.000    -.3422085   -.1776461
                Democracy  |   .0683143   .0254544     2.68   0.012     .0161733    .1204552
                    Drugs  |  -1.207677   .0748131   -16.14   0.000    -1.360924   -1.054429
                Education  |  -1.056814   .0667123   -15.84   0.000    -1.193468   -.9201596
                   Energy  |  -.9548055   .0935956   -10.20   0.000    -1.146527   -.7630836
              Environment  |  -.4711713   .0644302    -7.31   0.000    -.6031505   -.3391921
     Financial regulation  |  -.9019508   .0621751   -14.51   0.000    -1.029311   -.7745908
       Food & Agriculture  |  -.8374768   .0574304   -14.58   0.000    -.9551177    -.719836
                   Gender  |   .1276156   .0435077     2.93   0.007     .0384942    .2167369
          Good governance  |   .1240344   .0410073     3.02   0.005     .0400348    .2080339
                   Health  |  -.8059872   .0659049   -12.23   0.000    -.9409872   -.6709873
             Human rights  |  -.0279843   .0114113    -2.45   0.021    -.0513592   -.0046094
      ICT/Digital economy  |  -1.276458    .098464   -12.96   0.000    -1.478153   -1.074764
           Infrastructure  |  -.3241842   .0345508    -9.38   0.000    -.3949583   -.2534102
      Labour & Employment  |  -.8846091   .0782355   -11.31   0.000    -1.044867    -.724351
     Macroeconomic policy  |    -.23758   .0797528    -2.98   0.006    -.4009462   -.0742138
     Microeconomic policy  |  -.9596696   .0583805   -16.44   0.000    -1.079257   -.8400826
   Migration and refugees  |   .1556609   .0512739     3.04   0.005     .0506311    .2606907
         Nonproliferation  |  -1.382326    .114885   -12.03   0.000    -1.617657   -1.146994
           Nuclear safety  |  -1.234791   .0799846   -15.44   0.000    -1.398632    -1.07095
         Peace & security  |  -.6931608   .0474945   -14.59   0.000    -.7904489   -.5958727
        Regional security  |  -.8602394   .0753271   -11.42   0.000     -1.01454   -.7059389
            Social policy  |    -.73035   .0484247   -15.08   0.000    -.8295434   -.6311565
                Terrorism  |  -.9922652   .1096874    -9.05   0.000     -1.21695   -.7675807
                    Trade  |  -1.172315   .1011437   -11.59   0.000    -1.379498    -.965131
             Transparency  |   .2159321   .0712471     3.03   0.005     .0699889    .3618752
                           |
                     _cons |    1.01996   .0816041    12.50   0.000      .852801    1.187118
--------------------------------------------------------------------------------------------

. estimates store Modela24

. *Table
. esttab Modela21 Modela22 Modela23 Modela24 using TabA12_CtlDeltaDevCommits.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> note("Unit fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file TabA12_CtlDeltaDevCommits.csv not found)
(output written to TabA12_CtlDeltaDevCommits.csv)

. 
. *************************************************************************************************************************
. *Appendix G
. fracreg probit PerCRSspend l3.PerAnnCommits l2.PerAnnCommits l.PerAnnCommits PerAnnCommits f.PerAnnCommits f2.PerAnnCommits f3.PerAnnCommits 
> ///
> l.PerCRSspend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea i.year, vce(cluster policyArea_num)

Iteration 0:   log pseudolikelihood = -791.94113  
Iteration 1:   log pseudolikelihood = -11.656449  
Iteration 2:   log pseudolikelihood = -9.0800309  
Iteration 3:   log pseudolikelihood = -8.7814978  
Iteration 4:   log pseudolikelihood = -8.7770042  
Iteration 5:   log pseudolikelihood = -8.7769888  
Iteration 6:   log pseudolikelihood = -8.7769888  

Fractional probit regression                    Number of obs     =      1,141
                                                Wald chi2(28)     =   34520.52
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -8.7769888               Pseudo R2         =     0.1623

                               (Std. Err. adjusted for 30 clusters in policyArea_num)
-------------------------------------------------------------------------------------
                    |               Robust
        PerCRSspend |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      PerAnnCommits |
                L3. |  -1.323938   .8221873    -1.61   0.107    -2.935395    .2875197
                L2. |  -1.099857   .3872765    -2.84   0.005    -1.858905   -.3408088
                L1. |   1.043492   .5441081     1.92   0.055    -.0229406    2.109924
                --. |  -1.025465   .6995918    -1.47   0.143     -2.39664    .3457094
                F1. |  -.4219229   .5198666    -0.81   0.417    -1.440843    .5969969
                F2. |  -.3838253   .3169391    -1.21   0.226    -1.005014    .2373638
                F3. |  -.4546433   .3718964    -1.22   0.222    -1.183547    .2742602
                    |
        PerCRSspend |
                L1. |   43.82617   3.934039    11.14   0.000     36.11559    51.53674
                    |
 AllNewTFs_ThisArea |
                L1. |   .0205087   .0137401     1.49   0.136    -.0064213    .0474388
                    |
cumsumTFsInThisArea |
                L1. |    .000294   .0014764     0.20   0.842    -.0025996    .0031876
                    |
               year |
              1979  |  -.4201804   .0341044   -12.32   0.000    -.4870238    -.353337
              1980  |  -.2602359   .0779429    -3.34   0.001    -.4130012   -.1074705
              1981  |  -.0357748   .1765384    -0.20   0.839    -.3817838    .3102341
              1982  |  -.1286561   .0951909    -1.35   0.177    -.3152268    .0579145
              1983  |   .1692177   .0280492     6.03   0.000     .1142423    .2241931
              1984  |  -.1033964   .1619948    -0.64   0.523    -.4209004    .2141075
              1985  |   .2254836   .2616127     0.86   0.389    -.2872679    .7382352
              1986  |   .1137209   .1426013     0.80   0.425    -.1657725    .3932143
              1987  |   .1979039    .190791     1.04   0.300    -.1760396    .5718474
              1988  |    .099841   .1116693     0.89   0.371    -.1190268    .3187088
              1989  |   .2607648   .2035415     1.28   0.200    -.1381692    .6596989
              1990  |   .2064105   .1262844     1.63   0.102    -.0411024    .4539235
              1991  |   .2930455   .1241563     2.36   0.018     .0497036    .5363874
              1992  |   .3775535   .0935364     4.04   0.000     .1942254    .5608815
              1993  |   .1651122   .1569017     1.05   0.293    -.1424094    .4726338
              1994  |   .4322076   .0657067     6.58   0.000     .3034248    .5609904
              1995  |   .5565995    .112464     4.95   0.000      .336174     .777025
              1996  |   .4089012   .1411627     2.90   0.004     .1322273     .685575
              1997  |   .4244119   .1252389     3.39   0.001     .1789482    .6698757
              1998  |   .6071218    .079842     7.60   0.000     .4506345    .7636092
              1999  |   .3093611   .3770178     0.82   0.412    -.4295803    1.048302
              2000  |  -.0570409   .6138952    -0.09   0.926    -1.260253    1.146172
              2001  |   .5525419   .1008706     5.48   0.000     .3548392    .7502447
              2002  |    .517956   .1335633     3.88   0.000     .2561766    .7797353
              2003  |   .1660133   .3469428     0.48   0.632     -.513982    .8460087
              2004  |  -.0066619   .4578993    -0.01   0.988     -.904128    .8908041
              2005  |   .4967726   .2136172     2.33   0.020     .0780905    .9154547
              2006  |    .462032   .1496646     3.09   0.002     .1686948    .7553693
              2007  |    .395534   .2168138     1.82   0.068    -.0294132    .8204812
              2008  |    .619121   .1338093     4.63   0.000     .3568595    .8813825
              2009  |   .5031221   .1909498     2.63   0.008     .1288674    .8773768
              2010  |   .3015699    .317468     0.95   0.342     -.320656    .9237958
              2011  |   .3341391   .2468463     1.35   0.176    -.1496707    .8179489
              2012  |   .4489272   .2066754     2.17   0.030     .0438508    .8540035
              2013  |   .5793127   .1722652     3.36   0.001     .2416792    .9169463
              2014  |   .4774493   .1810996     2.64   0.008     .1225007    .8323979
              2015  |   .4633136   .1903996     2.43   0.015     .0901374    .8364899
              2016  |   .5717342   .1796724     3.18   0.001     .2195827    .9238856
              2017  |   .4541889   .2530882     1.79   0.073    -.0418548    .9502326
              2018  |   .5639057   .1827638     3.09   0.002     .2056952    .9221161
                    |
              _cons |  -3.580019   .2244371   -15.95   0.000    -4.019908    -3.14013
-------------------------------------------------------------------------------------

. estimates store PerApp1

. fracreg probit PerCRSspend l3.deltaPerAnnCommits l2.deltaPerAnnCommits l.deltaPerAnnCommits deltaPerAnnCommits f.deltaPerAnnCommits f2.deltaP
> erAnnCommits f3.deltaPerAnnCommits ///
> l.PerCRSspend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea i.year, vce(cluster policyArea_num)

Iteration 0:   log pseudolikelihood = -770.44586  
Iteration 1:   log pseudolikelihood = -11.567144  
Iteration 2:   log pseudolikelihood = -9.0480028  
Iteration 3:   log pseudolikelihood = -8.7421096  
Iteration 4:   log pseudolikelihood = -8.7373806  
Iteration 5:   log pseudolikelihood = -8.7373662  
Iteration 6:   log pseudolikelihood = -8.7373662  

Fractional probit regression                    Number of obs     =      1,110
                                                Wald chi2(28)     =  260164.05
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -8.7373662               Pseudo R2         =     0.1579

                               (Std. Err. adjusted for 30 clusters in policyArea_num)
-------------------------------------------------------------------------------------
                    |               Robust
        PerCRSspend |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
 deltaPerAnnCommits |
                L3. |   .3995504   .2547318     1.57   0.117    -.0997147    .8988155
                L2. |   .6300338   .3716627     1.70   0.090    -.0984116    1.358479
                L1. |   .8433845   .4102524     2.06   0.040     .0393045    1.647464
                --. |  -.1286364   .2883204    -0.45   0.655     -.693734    .4364612
                F1. |   .2969492    .259635     1.14   0.253    -.2119261    .8058245
                F2. |   .3236514   .3355089     0.96   0.335     -.333934    .9812367
                F3. |   .1806456   .2123967     0.85   0.395    -.2356443    .5969355
                    |
        PerCRSspend |
                L1. |   46.14326   4.162479    11.09   0.000     37.98495    54.30157
                    |
 AllNewTFs_ThisArea |
                L1. |   .0218904   .0129594     1.69   0.091    -.0035095    .0472904
                    |
cumsumTFsInThisArea |
                L1. |    .000414   .0015671     0.26   0.792    -.0026575    .0034855
                    |
               year |
              1980  |   .1463448   .1130232     1.29   0.195    -.0751766    .3678661
              1981  |   .3728963   .1992522     1.87   0.061    -.0176308    .7634234
              1982  |   .2838321   .1167993     2.43   0.015     .0549096    .5127547
              1983  |   .5561002   .0368329    15.10   0.000     .4839092    .6282913
              1984  |   .3014258   .1854285     1.63   0.104    -.0620073    .6648589
              1985  |   .6287818   .2906811     2.16   0.031     .0590573    1.198506
              1986  |   .5164659   .1654195     3.12   0.002     .1922496    .8406823
              1987  |   .5797417   .2112562     2.74   0.006     .1656871    .9937963
              1988  |   .4625572    .130063     3.56   0.000     .2076384     .717476
              1989  |   .6513065   .2174665     2.99   0.003     .2250799    1.077533
              1990  |   .5864709   .1364699     4.30   0.000     .3189948     .853947
              1991  |   .6680446   .1336292     5.00   0.000     .4061362     .929953
              1992  |   .7543061   .0920849     8.19   0.000      .573823    .9347891
              1993  |   .5436221   .1676313     3.24   0.001     .2150707    .8721735
              1994  |   .8028869    .056463    14.22   0.000     .6922214    .9135524
              1995  |   .9331699   .1263347     7.39   0.000     .6855584    1.180781
              1996  |   .7833934   .1567632     5.00   0.000     .4761433    1.090644
              1997  |   .7804659   .1490286     5.24   0.000     .4883752    1.072557
              1998  |   .9672164   .1011351     9.56   0.000     .7689951    1.165438
              1999  |   .6412105   .4281825     1.50   0.134    -.1980118    1.480433
              2000  |   .2407843   .6998377     0.34   0.731    -1.130872    1.612441
              2001  |   .9100628   .1075394     8.46   0.000     .6992895    1.120836
              2002  |   .8756376    .142583     6.14   0.000     .5961801    1.155095
              2003  |   .4796669   .4187538     1.15   0.252    -.3410754    1.300409
              2004  |   .3604159   .4871043     0.74   0.459     -.594291    1.315123
              2005  |   .8762187   .2240913     3.91   0.000     .4370079    1.315429
              2006  |   .8351674   .1528077     5.47   0.000     .5356698    1.134665
              2007  |   .7644466   .2260455     3.38   0.001     .3214055    1.207488
              2008  |   .9867581   .1388138     7.11   0.000      .714688    1.258828
              2009  |   .8827465   .1819925     4.85   0.000     .5260478    1.239445
              2010  |   .6707995   .3404591     1.97   0.049      .003512    1.338087
              2011  |   .6997817   .2572092     2.72   0.007      .195661    1.203902
              2012  |   .8132519   .2035123     4.00   0.000     .4143751    1.212129
              2013  |   .9398182   .1654929     5.68   0.000      .615458    1.264178
              2014  |   .8408371   .1807063     4.65   0.000     .4866592    1.195015
              2015  |   .8130162   .1908759     4.26   0.000     .4389063    1.187126
              2016  |   .9178951   .1693311     5.42   0.000     .5860122    1.249778
              2017  |    .786292    .254318     3.09   0.002     .2878378    1.284746
              2018  |    .907536   .1829024     4.96   0.000     .5490539    1.266018
                    |
              _cons |  -4.037568   .2191851   -18.42   0.000    -4.467163   -3.607973
-------------------------------------------------------------------------------------

. estimates store PerApp2

. *Make non G7 % Var & Test
. gen nonG7spendPerCRS = nonG7spend/TotalCRSAnnualSpend
(60 missing values generated)

. fracreg probit nonG7spendPerCRS l3.PerAnnCommits l2.PerAnnCommits l.PerAnnCommits PerAnnCommits f.PerAnnCommits f2.PerAnnCommits f3.PerAnnCom
> mits ///
> l.nonG7spendPerCRS l.AllNewTFs_ThisArea l.cumsumTFsInThisArea i.year, vce(cluster policyArea_num)

Iteration 0:   log pseudolikelihood = -791.92821  
Iteration 1:   log pseudolikelihood = -11.516949  
Iteration 2:   log pseudolikelihood = -8.9689739  
Iteration 3:   log pseudolikelihood = -8.6756125  
Iteration 4:   log pseudolikelihood = -8.6710399  
Iteration 5:   log pseudolikelihood = -8.6710116  
Iteration 6:   log pseudolikelihood = -8.6710115  

Fractional probit regression                    Number of obs     =      1,141
                                                Wald chi2(29)     =   7.08e+09
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -8.6710115               Pseudo R2         =     0.1635

                               (Std. Err. adjusted for 30 clusters in policyArea_num)
-------------------------------------------------------------------------------------
                    |               Robust
   nonG7spendPerCRS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      PerAnnCommits |
                L3. |  -1.386795   .8644445    -1.60   0.109    -3.081075    .3074847
                L2. |  -1.199499   .3828752    -3.13   0.002     -1.94992   -.4490773
                L1. |   1.073597   .5409823     1.98   0.047      .013291    2.133903
                --. |  -.9762253   .6933896    -1.41   0.159    -2.335244    .3827934
                F1. |  -.3593923   .5138914    -0.70   0.484    -1.366601    .6478164
                F2. |  -.4857418   .3528782    -1.38   0.169     -1.17737    .2058867
                F3. |  -.4888433   .3853305    -1.27   0.205    -1.244077    .2663905
                    |
   nonG7spendPerCRS |
                L1. |   43.72091   3.966635    11.02   0.000     35.94645    51.49537
                    |
 AllNewTFs_ThisArea |
                L1. |   .0218182   .0141544     1.54   0.123    -.0059239    .0495603
                    |
cumsumTFsInThisArea |
                L1. |   .0002301   .0015008     0.15   0.878    -.0027115    .0031717
                    |
               year |
              1979  |  -.6007386   .0308825   -19.45   0.000    -.6612672   -.5402099
              1980  |  -.2597947   .0766942    -3.39   0.001    -.4101126   -.1094767
              1981  |  -.0425481   .1732029    -0.25   0.806    -.3820195    .2969233
              1982  |  -.1421678   .0923291    -1.54   0.124    -.3231295    .0387938
              1983  |   .1297532   .0437975     2.96   0.003     .0439118    .2155947
              1984  |  -.1059053   .1651042    -0.64   0.521    -.4295037     .217693
              1985  |   .2255755   .2616802     0.86   0.389    -.2873082    .7384592
              1986  |   .1079928   .1399444     0.77   0.440    -.1662932    .3822788
              1987  |   .1992877   .1910551     1.04   0.297    -.1751734    .5737488
              1988  |   .0919277   .1108911     0.83   0.407    -.1254148    .3092702
              1989  |   .2484146   .2058792     1.21   0.228    -.1551012    .6519303
              1990  |   .1442864    .161127     0.90   0.371    -.1715167    .4600895
              1991  |   .3001732   .1199027     2.50   0.012     .0651682    .5351781
              1992  |   .3775666   .0939869     4.02   0.000     .1933557    .5617774
              1993  |   .1651719     .15623     1.06   0.290    -.1410333     .471377
              1994  |   .4328329   .0658916     6.57   0.000     .3036877    .5619782
              1995  |   .5577642   .1124659     4.96   0.000     .3373351    .7781933
              1996  |   .4096594    .142382     2.88   0.004     .1305958    .6887231
              1997  |   .4179806    .123506     3.38   0.001     .1759133     .660048
              1998  |   .6153024   .0788896     7.80   0.000     .4606815    .7699232
              1999  |   .3102483   .3792712     0.82   0.413    -.4331095    1.053606
              2000  |  -.0552101   .6163765    -0.09   0.929    -1.263286    1.152866
              2001  |   .5518857   .1019959     5.41   0.000     .3519773    .7517941
              2002  |   .5192992   .1333652     3.89   0.000     .2579083    .7806902
              2003  |   .1701126   .3478216     0.49   0.625    -.5116052    .8518303
              2004  |  -.0049249   .4594325    -0.01   0.991     -.905396    .8955463
              2005  |   .4987993   .2145649     2.32   0.020     .0782599    .9193388
              2006  |   .4607728   .1492113     3.09   0.002     .1683241    .7532215
              2007  |   .3725811   .2151972     1.73   0.083    -.0491977    .7943599
              2008  |    .629548   .1312679     4.80   0.000     .3722676    .8868284
              2009  |   .5001326   .1911167     2.62   0.009     .1255507    .8747145
              2010  |    .300465   .3177986     0.95   0.344    -.3224088    .9233389
              2011  |   .2825565   .2324447     1.22   0.224    -.1730268    .7381398
              2012  |   .4562024   .2082263     2.19   0.028     .0480864    .8643184
              2013  |   .5770021    .173987     3.32   0.001     .2359939    .9180103
              2014  |   .4818449   .1780806     2.71   0.007     .1328133    .8308765
              2015  |   .4521145   .1920426     2.35   0.019     .0757179    .8285111
              2016  |   .5816627   .1760095     3.30   0.001     .2366904     .926635
              2017  |   .4570296   .2548038     1.79   0.073    -.0423766    .9564358
              2018  |   .5635641   .1819379     3.10   0.002     .2069723    .9201559
                    |
              _cons |  -3.578781   .2242605   -15.96   0.000    -4.018324   -3.139238
-------------------------------------------------------------------------------------

. estimates store PerApp3

. fracreg probit nonG7spendPerCRS l3.deltaPerAnnCommits l2.deltaPerAnnCommits l.deltaPerAnnCommits deltaPerAnnCommits f.deltaPerAnnCommits f2.d
> eltaPerAnnCommits f3.deltaPerAnnCommits ///
> l.nonG7spendPerCRS l.AllNewTFs_ThisArea l.cumsumTFsInThisArea i.year, vce(cluster policyArea_num)

Iteration 0:   log pseudolikelihood = -770.43294  
Iteration 1:   log pseudolikelihood = -11.427655  
Iteration 2:   log pseudolikelihood = -8.9387515  
Iteration 3:   log pseudolikelihood = -8.6386893  
Iteration 4:   log pseudolikelihood = -8.6339371  
Iteration 5:   log pseudolikelihood = -8.6339151  
Iteration 6:   log pseudolikelihood =  -8.633915  

Fractional probit regression                    Number of obs     =      1,110
                                                Wald chi2(29)     =   1.38e+11
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -8.633915               Pseudo R2         =     0.1588

                               (Std. Err. adjusted for 30 clusters in policyArea_num)
-------------------------------------------------------------------------------------
                    |               Robust
   nonG7spendPerCRS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
 deltaPerAnnCommits |
                L3. |   .3554579   .2510105     1.42   0.157    -.1365136    .8474295
                L2. |   .6089951   .3919505     1.55   0.120    -.1592138    1.377204
                L1. |   .8726041   .4165064     2.10   0.036     .0562665    1.688942
                --. |  -.1165446   .2900242    -0.40   0.688    -.6849816    .4518923
                F1. |   .2822171   .2660842     1.06   0.289    -.2392984    .8037325
                F2. |   .2622419   .3487283     0.75   0.452    -.4212529    .9457367
                F3. |   .1715936   .2138769     0.80   0.422    -.2475974    .5907845
                    |
   nonG7spendPerCRS |
                L1. |   46.08581   4.232797    10.89   0.000     37.78968    54.38194
                    |
 AllNewTFs_ThisArea |
                L1. |   .0231376   .0132357     1.75   0.080    -.0028039     .049079
                    |
cumsumTFsInThisArea |
                L1. |   .0003502   .0015991     0.22   0.827     -.002784    .0034844
                    |
               year |
              1980  |   .3244514   .0961831     3.37   0.001     .1359359    .5129668
              1981  |   .5457386    .175512     3.11   0.002     .2017414    .8897359
              1982  |   .4511122   .0966418     4.67   0.000     .2616977    .6405267
              1983  |   .6955904   .0307509    22.62   0.000     .6353198     .755861
              1984  |   .4785926    .168348     2.84   0.004     .1486367    .8085486
              1985  |   .8077181   .2711329     2.98   0.003     .2763074    1.339129
              1986  |   .6889885   .1423605     4.84   0.000     .4099671      .96801
              1987  |   .7591007   .1921841     3.95   0.000     .3824268    1.135775
              1988  |   .6326081   .1082487     5.84   0.000     .4204445    .8447717
              1989  |    .817636      .2012     4.06   0.000     .4232912    1.211981
              1990  |   .7023107   .1556753     4.51   0.000     .3971928    1.007429
              1991  |   .8527264   .1136993     7.50   0.000     .6298798    1.075573
              1992  |    .931845   .0825745    11.28   0.000     .7700019    1.093688
              1993  |   .7218141   .1501483     4.81   0.000     .4275289    1.016099
              1994  |   .9813561   .0517165    18.98   0.000     .8799936    1.082719
              1995  |   1.111774   .1100693    10.10   0.000     .8960416    1.327505
              1996  |   .9620197   .1386802     6.94   0.000     .6902115    1.233828
              1997  |   .9514042   .1305884     7.29   0.000     .6954556    1.207353
              1998  |   1.152205   .0829004    13.90   0.000     .9897229    1.314686
              1999  |   .8186356   .4127624     1.98   0.047     .0096362    1.627635
              2000  |   .4198447   .6849106     0.61   0.540    -.9225554    1.762245
              2001  |   1.086436   .0911694    11.92   0.000     .9077476    1.265125
              2002  |   1.053397   .1297371     8.12   0.000     .7991171    1.307677
              2003  |    .660053   .4028865     1.64   0.101    -.1295901    1.449696
              2004  |   .5382706    .470572     1.14   0.253    -.3840336    1.460575
              2005  |   1.055881   .2077197     5.08   0.000     .6487584    1.463005
              2006  |   1.011843   .1378244     7.34   0.000     .7417126    1.281974
              2007  |   .9192489   .2058058     4.47   0.000      .515877    1.322621
              2008  |    1.17475    .125401     9.37   0.000     .9289688    1.420532
              2009  |   1.058139   .1772121     5.97   0.000     .7108094    1.405468
              2010  |   .8480435   .3244473     2.61   0.009     .2121384    1.483949
              2011  |   .8287333   .2296443     3.61   0.000     .3786388    1.278828
              2012  |   1.000659   .1977597     5.06   0.000     .6130566     1.38826
              2013  |   1.114887   .1583254     7.04   0.000     .8045754    1.425199
              2014  |   1.023031    .166599     6.14   0.000     .6965024    1.349559
              2015  |   .9794409   .1789002     5.47   0.000      .628803    1.330079
              2016  |   1.104647   .1620188     6.82   0.000     .7870964    1.422198
              2017  |   .9664045   .2418641     4.00   0.000     .4923597    1.440449
              2018  |   1.085124   .1762923     6.16   0.000     .7395976    1.430651
                    |
              _cons |  -4.216429   .2007952   -21.00   0.000     -4.60998   -3.822878
-------------------------------------------------------------------------------------

. estimates store PerApp4

. **TFs
. gen TFsThisArea_PerTotalTFs = AllNewTFs_ThisArea/NewTFs_Total
(60 missing values generated)

. fracreg probit TFsThisArea_PerTotalTFs l3.PerAnnCommits l2.PerAnnCommits l.PerAnnCommits PerAnnCommits f.PerAnnCommits f2.PerAnnCommits f3.Pe
> rAnnCommits ///
> l.PerCRSspend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea i.year, vce(cluster policyArea_num)

Iteration 0:   log pseudolikelihood = -778.64419  
Iteration 1:   log pseudolikelihood = -160.51468  
Iteration 2:   log pseudolikelihood = -151.39431  
Iteration 3:   log pseudolikelihood = -151.27092  
Iteration 4:   log pseudolikelihood = -151.27081  
Iteration 5:   log pseudolikelihood = -151.27081  

Fractional probit regression                    Number of obs     =      1,085
                                                Wald chi2(28)     =    4463.37
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -151.27081               Pseudo R2         =     0.0387

                                   (Std. Err. adjusted for 30 clusters in policyArea_num)
-----------------------------------------------------------------------------------------
                        |               Robust
TFsThisArea_PerTotalTFs |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
          PerAnnCommits |
                    L3. |  -.2206387   .3962483    -0.56   0.578    -.9972712    .5559937
                    L2. |  -.3781143   .5400218    -0.70   0.484    -1.436537     .680309
                    L1. |   -.869363   .3712739    -2.34   0.019    -1.597047   -.1416795
                    --. |   1.500317   .4920588     3.05   0.002      .535899    2.464734
                    F1. |    .183733   .5320975     0.35   0.730    -.8591589    1.226625
                    F2. |  -.0375876   .3559907    -0.11   0.916    -.7353166    .6601414
                    F3. |  -.4008987   .2634521    -1.52   0.128    -.9172553    .1154579
                        |
            PerCRSspend |
                    L1. |   .5691536   1.898593     0.30   0.764     -3.15202    4.290327
                        |
     AllNewTFs_ThisArea |
                    L1. |   .0775132   .0115854     6.69   0.000     .0548062    .1002201
                        |
    cumsumTFsInThisArea |
                    L1. |   .0048617   .0006951     6.99   0.000     .0034994     .006224
                        |
                   year |
                  1980  |   .0385551    .226774     0.17   0.865    -.4059138    .4830241
                  1981  |  -.0135337   .0861524    -0.16   0.875    -.1823893     .155322
                  1982  |  -.0825671   .0683114    -1.21   0.227     -.216455    .0513208
                  1983  |  -.0706844   .0964695    -0.73   0.464    -.2597612    .1183924
                  1985  |  -.0032731   .1100432    -0.03   0.976    -.2189539    .2124076
                  1986  |  -.0425054    .257197    -0.17   0.869    -.5466022    .4615915
                  1987  |  -.0200415    .097652    -0.21   0.837    -.2114359    .1713528
                  1988  |  -.0074965   .4794498    -0.02   0.988    -.9472009    .9322079
                  1989  |  -.0174992   .0737037    -0.24   0.812    -.1619559    .1269575
                  1990  |  -.1239584   .3513129    -0.35   0.724    -.8125191    .5646022
                  1991  |  -.0384475   .0690804    -0.56   0.578    -.1738425    .0969476
                  1992  |  -.1833874    .079363    -2.31   0.021    -.3389359   -.0278388
                  1993  |  -.1718736   .0744125    -2.31   0.021    -.3177195   -.0260278
                  1994  |   -.145005   .0813977    -1.78   0.075    -.3045415    .0145315
                  1995  |  -.1351165   .0936638    -1.44   0.149    -.3186942    .0484611
                  1996  |  -.0869529   .0891876    -0.97   0.330    -.2617575    .0878516
                  1997  |  -.1242401   .2272097    -0.55   0.585    -.5695631    .3210828
                  1998  |  -.0778304   .0745628    -1.04   0.297    -.2239709    .0683101
                  1999  |  -.1811795   .0824309    -2.20   0.028    -.3427411    -.019618
                  2000  |  -.3363266    .078103    -4.31   0.000    -.4894056   -.1832475
                  2001  |  -.3221247   .1194156    -2.70   0.007     -.556175   -.0880743
                  2002  |  -.3494201   .0719549    -4.86   0.000    -.4904491   -.2083911
                  2003  |  -.3205018   .0725932    -4.42   0.000    -.4627819   -.1782217
                  2004  |  -.4544498   .0711072    -6.39   0.000    -.5938174   -.3150822
                  2005  |  -.2231119   .0911246    -2.45   0.014    -.4017127    -.044511
                  2006  |   -.473321   .1213493    -3.90   0.000    -.7111613   -.2354807
                  2007  |  -.3590551   .1275116    -2.82   0.005    -.6089733   -.1091369
                  2008  |  -.4330839   .0799608    -5.42   0.000    -.5898041   -.2763637
                  2009  |  -.6997131   .1553784    -4.50   0.000    -1.004249    -.395177
                  2010  |  -.6006813   .0936818    -6.41   0.000    -.7842944   -.4170683
                  2011  |   -.780924   .1249828    -6.25   0.000    -1.025886   -.5359622
                  2012  |  -.6874094   .1912619    -3.59   0.000    -1.062276   -.3125429
                  2013  |  -.6040276   .0937669    -6.44   0.000    -.7878073   -.4202478
                  2014  |  -.7292354   .0991733    -7.35   0.000    -.9236115   -.5348592
                  2015  |  -.5202896   .1275482    -4.08   0.000    -.7702796   -.2702997
                  2016  |  -.6293392   .1679126    -3.75   0.000    -.9584419   -.3002366
                  2017  |  -.6061097   .1023282    -5.92   0.000    -.8066694     -.40555
                  2018  |  -.7664379   .1451214    -5.28   0.000    -1.050871   -.4820053
                        |
                  _cons |  -1.852529   .0997575   -18.57   0.000     -2.04805   -1.657008
-----------------------------------------------------------------------------------------

. estimates store PerApp5

. fracreg probit TFsThisArea_PerTotalTFs l3.deltaPerAnnCommits l2.deltaPerAnnCommits l.deltaPerAnnCommits deltaPerAnnCommits f.deltaPerAnnCommi
> ts f2.deltaPerAnnCommits f3.deltaPerAnnCommits ///
> l.PerCRSspend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea i.year, vce(cluster policyArea_num)

Iteration 0:   log pseudolikelihood = -756.40277  
Iteration 1:   log pseudolikelihood = -155.94545  
Iteration 2:   log pseudolikelihood = -147.01879  
Iteration 3:   log pseudolikelihood = -146.90219  
Iteration 4:   log pseudolikelihood = -146.90209  
Iteration 5:   log pseudolikelihood = -146.90209  

Fractional probit regression                    Number of obs     =      1,054
                                                Wald chi2(28)     =    2366.38
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -146.90209               Pseudo R2         =     0.0396

                                   (Std. Err. adjusted for 30 clusters in policyArea_num)
-----------------------------------------------------------------------------------------
                        |               Robust
TFsThisArea_PerTotalTFs |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
     deltaPerAnnCommits |
                    L3. |   .0423185    .371143     0.11   0.909    -.6851083    .7697454
                    L2. |   .2955147   .5329966     0.55   0.579    -.7491394    1.340169
                    L1. |    .565005   .5565782     1.02   0.310    -.5258681    1.655878
                    --. |   1.452635   .5826851     2.49   0.013     .3105931    2.594677
                    F1. |   .0567076   .4415916     0.13   0.898    -.8087961    .9222113
                    F2. |  -.3656151   .3664534    -1.00   0.318     -1.08385    .3526203
                    F3. |  -.3835159   .2193364    -1.75   0.080    -.8134073    .0463755
                        |
            PerCRSspend |
                    L1. |   .6698109   1.668068     0.40   0.688    -2.599542    3.939164
                        |
     AllNewTFs_ThisArea |
                    L1. |   .0791132   .0114768     6.89   0.000     .0566192    .1016073
                        |
    cumsumTFsInThisArea |
                    L1. |   .0047701    .000662     7.21   0.000     .0034726    .0060676
                        |
                   year |
                  1981  |  -.0522327   .2569737    -0.20   0.839    -.5558919    .4514265
                  1982  |  -.1228609   .2291345    -0.54   0.592    -.5719563    .3262344
                  1983  |  -.1079119   .2391308    -0.45   0.652    -.5765996    .3607758
                  1985  |   -.041555    .182944    -0.23   0.820    -.4001187    .3170086
                  1986  |  -.0846931   .1040588    -0.81   0.416    -.2886446    .1192584
                  1987  |  -.0590203   .2445155    -0.24   0.809    -.5382619    .4202213
                  1988  |  -.0457836   .5035633    -0.09   0.928     -1.03275    .9411823
                  1989  |  -.0573291   .2397877    -0.24   0.811    -.5273043     .412646
                  1990  |   -.168744   .3772309    -0.45   0.655    -.9081029    .5706149
                  1991  |  -.0773487   .2367226    -0.33   0.744    -.5413165    .3866192
                  1992  |  -.2238969   .1823833    -1.23   0.220    -.5813616    .1335679
                  1993  |  -.2130453   .2442925    -0.87   0.383    -.6918498    .2657591
                  1994  |  -.1840921   .2259673    -0.81   0.415      -.62698    .2587958
                  1995  |  -.1744013   .1997356    -0.87   0.383    -.5658759    .2170732
                  1996  |  -.1267561   .1797219    -0.71   0.481    -.4790047    .2254924
                  1997  |  -.1649699   .1901337    -0.87   0.386    -.5376251    .2076853
                  1998  |  -.1151873    .203711    -0.57   0.572    -.5144536     .284079
                  1999  |  -.2212272   .1825571    -1.21   0.226    -.5790326    .1365781
                  2000  |  -.3789909   .2175083    -1.74   0.081    -.8052994    .0473176
                  2001  |  -.3623595   .1859398    -1.95   0.051    -.7267948    .0020758
                  2002  |  -.3918785   .2062524    -1.90   0.057    -.7961259    .0123688
                  2003  |  -.3602423   .2151523    -1.67   0.094    -.7819331    .0614484
                  2004  |  -.4960307      .2098    -2.36   0.018    -.9072311   -.0848302
                  2005  |  -.2612449   .1846208    -1.42   0.157     -.623095    .1006052
                  2006  |  -.5156182   .1694859    -3.04   0.002    -.8478045   -.1834319
                  2007  |  -.3998777   .1834234    -2.18   0.029    -.7593809   -.0403745
                  2008  |  -.4739583   .1918103    -2.47   0.013    -.8498996   -.0980171
                  2009  |  -.7445101   .1630984    -4.56   0.000    -1.064177   -.4248431
                  2010  |  -.6416265   .1865549    -3.44   0.001    -1.007267   -.2759856
                  2011  |  -.8220265   .2007335    -4.10   0.000    -1.215457    -.428596
                  2012  |  -.7273818   .1901033    -3.83   0.000    -1.099977   -.3547863
                  2013  |  -.6407685   .1710649    -3.75   0.000    -.9760494   -.3054875
                  2014  |  -.7665802    .182294    -4.21   0.000     -1.12387   -.4092905
                  2015  |  -.5584956   .1525456    -3.66   0.000    -.8574794   -.2595118
                  2016  |   -.670237   .1222056    -5.48   0.000    -.9097556   -.4307184
                  2017  |  -.6333448     .19584    -3.23   0.001    -1.017184   -.2495054
                  2018  |  -.8034842   .1488864    -5.40   0.000    -1.095296   -.5116721
                        |
                  _cons |  -1.819551   .1832414    -9.93   0.000    -2.178697   -1.460404
-----------------------------------------------------------------------------------------

. estimates store PerApp6

. *Table
. esttab PerApp1 PerApp2 PerApp3 PerApp4 PerApp5 PerApp6 using TabA6_PercentVars.csv, replace /// 
> varlabels("deltaPerAnnCommits" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea" "
> TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> title("Percentage Measures") /// 
> note("Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file TabA6_PercentVars.csv not found)
(output written to TabA6_PercentVars.csv)

. 
. *************************************************************************************************************************
. *Appendix H (IPOD vars)
. *Only keep IPOD years (1980-2015)
. drop if year < 1980
(150 observations deleted)

. drop if year > 2015
(180 observations deleted)

. *Spend
. *Model 1 (Correlation)
. reghdfe deltaCommitSpend CommitNoIncreaseDummy IPODareaCount ///
> , absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =      1,010
Absorbing 2 HDFE groups                           F(   2,     28) =       2.91
Statistics robust to heteroskedasticity           Prob > F        =     0.0714
                                                  R-squared       =     0.0543
                                                  Adj R-squared   =    -0.0118
                                                  Within R-sq.    =     0.0010
Number of clusters (policyArea_num) =         29  Root MSE        =   335.2164

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   25.01111   11.26532     2.22   0.035     1.935156    48.08707
        IPODareaCount |    .052419   .1593892     0.33   0.745    -.2740748    .3789129
                _cons |   4.191331   9.632622     0.44   0.667     -15.5402    23.92286
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        36           0          36     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model1

. *Model 2 (Add Controls)
. reghdfe deltaCommitSpend CommitNoIncreaseDummy IPODareaCount ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =        982
Absorbing 2 HDFE groups                           F(   4,     28) =       3.20
Statistics robust to heteroskedasticity           Prob > F        =     0.0276
                                                  R-squared       =     0.0655
                                                  Adj R-squared   =    -0.0030
                                                  Within R-sq.    =     0.0127
Number of clusters (policyArea_num) =         29  Root MSE        =   338.4743

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   26.68566   11.11861     2.40   0.023     3.910224    49.46109
        IPODareaCount |   .0747971   .1541814     0.49   0.631    -.2410291    .3906233
                      |
     deltaCommitSpend |
                  L1. |   -.112513   .0730923    -1.54   0.135    -.2622358    .0372098
                      |
   AllNewTFs_ThisArea |
                  L1. |   1.210366   5.549725     0.22   0.829    -10.15773    12.57846
                      |
                _cons |   2.110067   12.48337     0.17   0.867    -23.46095    27.68109
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        35           0          35     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model2

. *Model 3 (All Controls)
. reghdfe deltaCommitSpend CommitNoIncreaseDummy IPODareaCount ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =        982
Absorbing 2 HDFE groups                           F(   5,     28) =       2.44
Statistics robust to heteroskedasticity           Prob > F        =     0.0590
                                                  R-squared       =     0.0656
                                                  Adj R-squared   =    -0.0039
                                                  Within R-sq.    =     0.0128
Number of clusters (policyArea_num) =         29  Root MSE        =   338.6306

                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
     deltaCommitSpend |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   26.75311   11.10865     2.41   0.023      3.99806    49.50815
        IPODareaCount |   .0831198   .1667027     0.50   0.622    -.2583552    .4245947
                      |
     deltaCommitSpend |
                  L1. |  -.1127291   .0727952    -1.55   0.133    -.2618434    .0363852
                      |
   AllNewTFs_ThisArea |
                  L1. |  -.9594274   10.29929    -0.09   0.926    -22.05656    20.13771
                      |
  cumsumTFsInThisArea |
                  L1. |   .3630203   .8502015     0.43   0.673    -1.378539    2.104579
                      |
                _cons |   -2.12264    14.0412    -0.15   0.881    -30.88474    26.63946
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        35           0          35     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model3

. *Table
. esttab Model1 Model2 Model3 using TabA7_IPODspend.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> title("Spend models with IPOD") /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file TabA7_IPODspend.csv not found)
(output written to TabA7_IPODspend.csv)

. *TFs
. *Model 7 (Correlation)
. ppmlhdfe AllNewTFs_ThisArea CommitNoIncreaseDummy IPODareaCount ///
> , absorb(policyArea_num year) cluster(policyArea_num)
(dropped 28 observations that are either singletons or separated by a fixed effect)
Iteration 1:   deviance = 8.2812e+02  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -3.21  P   
Iteration 2:   deviance = 6.9544e+02  eps = 1.91e-01  iters = 3    tol = 1.0e-04  min(eta) =  -4.94      
Iteration 3:   deviance = 6.7279e+02  eps = 3.37e-02  iters = 3    tol = 1.0e-04  min(eta) =  -6.55      
Iteration 4:   deviance = 6.6973e+02  eps = 4.57e-03  iters = 2    tol = 1.0e-04  min(eta) =  -7.72      
Iteration 5:   deviance = 6.6951e+02  eps = 3.33e-04  iters = 2    tol = 1.0e-04  min(eta) =  -8.24      
Iteration 6:   deviance = 6.6950e+02  eps = 5.74e-06  iters = 2    tol = 1.0e-04  min(eta) =  -8.32      
Iteration 7:   deviance = 6.6950e+02  eps = 3.14e-09  iters = 2    tol = 1.0e-05  min(eta) =  -8.32   S  
Iteration 8:   deviance = 6.6950e+02  eps = 1.19e-15  iters = 2    tol = 1.0e-07  min(eta) =  -8.32   S  
Iteration 9:   deviance = 6.6950e+02  eps = 1.98e-16  iters = 1    tol = 1.0e-09  min(eta) =  -8.32   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 9 iterations and 21 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =        982
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(2)    =      19.63
Deviance             =  669.5016063               Prob > chi2     =     0.0001
Log pseudolikelihood = -1099.259782               Pseudo R2       =     0.5006

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .0967294   .0538942     1.79   0.073    -.0089014    .2023601
        IPODareaCount |  -.0031364   .0007177    -4.37   0.000    -.0045431   -.0017298
                _cons |   1.336557   .0376652    35.49   0.000     1.262735     1.41038
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        35           0          35     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model7

. *Model 8 (Add Controls)
. ppmlhdfe AllNewTFs_ThisArea CommitNoIncreaseDummy IPODareaCount ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(dropped 28 observations that are either singletons or separated by a fixed effect)
Iteration 1:   deviance = 8.0206e+02  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -3.19  P   
Iteration 2:   deviance = 6.6460e+02  eps = 2.07e-01  iters = 3    tol = 1.0e-04  min(eta) =  -5.07      
Iteration 3:   deviance = 6.4182e+02  eps = 3.55e-02  iters = 3    tol = 1.0e-04  min(eta) =  -6.73      
Iteration 4:   deviance = 6.3875e+02  eps = 4.81e-03  iters = 3    tol = 1.0e-04  min(eta) =  -7.91      
Iteration 5:   deviance = 6.3852e+02  eps = 3.53e-04  iters = 2    tol = 1.0e-04  min(eta) =  -8.44      
Iteration 6:   deviance = 6.3852e+02  eps = 5.94e-06  iters = 2    tol = 1.0e-04  min(eta) =  -8.52      
Iteration 7:   deviance = 6.3852e+02  eps = 2.98e-09  iters = 2    tol = 1.0e-05  min(eta) =  -8.52   S  
Iteration 8:   deviance = 6.3852e+02  eps = 1.04e-15  iters = 2    tol = 1.0e-07  min(eta) =  -8.52   S  
Iteration 9:   deviance = 6.3852e+02  eps = 0.00e+00  iters = 1    tol = 1.0e-09  min(eta) =  -8.52   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 9 iterations and 22 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =        954
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(4)    =      43.38
Deviance             =  638.5211981               Prob > chi2     =     0.0000
Log pseudolikelihood = -1078.769578               Pseudo R2       =     0.4992

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .0972263   .0477306     2.04   0.042      .003676    .1907766
        IPODareaCount |  -.0032422   .0006279    -5.16   0.000    -.0044729   -.0020116
                      |
     deltaCommitSpend |
                  L1. |   .0000999   .0000317     3.15   0.002     .0000378    .0001619
                      |
   AllNewTFs_ThisArea |
                  L1. |  -.0620327   .0214905    -2.89   0.004    -.1041533    -.019912
                      |
                _cons |   1.583123   .0880118    17.99   0.000     1.410623    1.755623
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        34           0          34     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model8

. *Model 9 (All Controls)
. ppmlhdfe AllNewTFs_ThisArea CommitNoIncreaseDummy IPODareaCount ///
> l.deltaCommitSpend l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
(dropped 28 observations that are either singletons or separated by a fixed effect)
Iteration 1:   deviance = 8.0563e+02  eps = .         iters = 4    tol = 1.0e-04  min(eta) =  -3.18  P   
Iteration 2:   deviance = 6.3644e+02  eps = 2.66e-01  iters = 3    tol = 1.0e-04  min(eta) =  -5.34      
Iteration 3:   deviance = 6.0569e+02  eps = 5.08e-02  iters = 3    tol = 1.0e-04  min(eta) =  -7.23      
Iteration 4:   deviance = 6.0208e+02  eps = 5.99e-03  iters = 3    tol = 1.0e-04  min(eta) =  -8.49      
Iteration 5:   deviance = 6.0182e+02  eps = 4.32e-04  iters = 3    tol = 1.0e-04  min(eta) =  -9.05      
Iteration 6:   deviance = 6.0182e+02  eps = 8.15e-06  iters = 2    tol = 1.0e-04  min(eta) =  -9.14      
Iteration 7:   deviance = 6.0182e+02  eps = 5.40e-09  iters = 2    tol = 1.0e-05  min(eta) =  -9.14   S  
Iteration 8:   deviance = 6.0182e+02  eps = 3.32e-15  iters = 2    tol = 1.0e-07  min(eta) =  -9.14   S  
Iteration 9:   deviance = 6.0182e+02  eps = 0.00e+00  iters = 1    tol = 1.0e-09  min(eta) =  -9.14   S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out   s: exact solver   h: step-halving   o: epsilon below tolerance)
Converged in 9 iterations and 23 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =        954
Absorbing 2 HDFE groups                           Residual df     =         28
Statistics robust to heteroskedasticity           Wald chi2(5)    =      48.59
Deviance             =  601.8151739               Prob > chi2     =     0.0000
Log pseudolikelihood = -1060.416566               Pseudo R2       =     0.5077

Number of clusters (policyArea_num)=        29
                                 (Std. Err. adjusted for 29 clusters in policyArea_num)
---------------------------------------------------------------------------------------
                      |               Robust
   AllNewTFs_ThisArea |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   .0800332    .043455     1.84   0.066     -.005137    .1652034
        IPODareaCount |  -.0043918   .0011935    -3.68   0.000     -.006731   -.0020526
                      |
     deltaCommitSpend |
                  L1. |   .0001008   .0000316     3.19   0.001     .0000389    .0001626
                      |
   AllNewTFs_ThisArea |
                  L1. |  -.0204532    .019356    -1.06   0.291    -.0583903    .0174839
                      |
  cumsumTFsInThisArea |
                  L1. |  -.0142575   .0022949    -6.21   0.000    -.0187554   -.0097597
                      |
                _cons |   2.211533   .1602699    13.80   0.000      1.89741    2.525656
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        34           0          34     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model9

. *Table
. esttab Model7 Model8 Model9 using TabA9_IPODtfs.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> title("IPOD Controls in Trust Fund Models") /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file TabA9_IPODtfs.csv not found)
(output written to TabA9_IPODtfs.csv)

. 
. *************************************************************************************************************************
. *Appendix I
. * Reload full data after dropping non-IPOD observations
. use "CRSg7frame.dta", clear

. xtset policyArea_num year
       panel variable:  policyArea_num (strongly balanced)
        time variable:  year, 1975 to 2021
                delta:  1 unit

. *drop 4 G7RG categories don't use
. drop if PolicyArea == "East-West (Russia)" | PolicyArea == "IFI/UN reform" | PolicyArea == "International cooperation" | PolicyArea == "Devel
> opment"
(188 observations deleted)

. *Correlation, IV
. ivreghdfe deltaSpendNoHost (CommitNoIncreaseDummy=USDsumHostSpend_byG7commit) ///
> , absorb(policyArea_num year) cluster(policyArea_num)
(MWFE estimator converged in 4 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on policyArea_num

Number of clusters (policyArea_num) =     29          Number of obs =     1296
                                                      F(  1,    28) =     5.10
                                                      Prob > F      =   0.0319
Total (centered) SS     =  197671346.9                Centered R2   =  -8.5695
Total (uncentered) SS   =  197671346.9                Uncentered R2 =  -8.5695
Residual SS             =   1891608625                Root MSE      =     1231

---------------------------------------------------------------------------------------
                      |               Robust
     deltaSpendNoHost |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   2929.721   1297.151     2.26   0.032      272.628    5586.814
---------------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              1.442
                                                   Chi-sq(1) P-val =    0.2299
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):                1.639
                         (Kleibergen-Paap rk Wald F statistic):          5.286
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         CommitNoIncreaseDummy
Excluded instruments: USDsumHostSpend_byG7commit
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        46           0          46     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model4

. *Add Controls, IV
. ivreghdfe deltaSpendNoHost (CommitNoIncreaseDummy=USDsumHostSpend_byG7commit) ///
> l.deltaSpendNoHost l.AllNewTFs_ThisArea, absorb(policyArea_num year) cluster(policyArea_num)
Warning: time variable year has 2 gap(s) in relevant range
(MWFE estimator converged in 4 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on policyArea_num

Number of clusters (policyArea_num) =     29          Number of obs =     1268
                                                      F(  3,    28) =     9.13
                                                      Prob > F      =   0.0002
Total (centered) SS     =  197668839.8                Centered R2   =  -5.5857
Total (uncentered) SS   =  197668839.8                Uncentered R2 =  -5.5857
Residual SS             =   1301780017                Root MSE      =     1033

---------------------------------------------------------------------------------------
                      |               Robust
     deltaSpendNoHost |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   2395.883   1053.623     2.27   0.031     237.6332    4554.133
                      |
     deltaSpendNoHost |
                  L1. |  -.2618456   .0512206    -5.11   0.000    -.3667662    -.156925
                      |
   AllNewTFs_ThisArea |
                  L1. |  -32.09549   29.28607    -1.10   0.282    -92.08527     27.8943
---------------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              1.435
                                                   Chi-sq(1) P-val =    0.2310
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):                1.603
                         (Kleibergen-Paap rk Wald F statistic):          4.764
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         CommitNoIncreaseDummy
Included instruments: L.deltaSpendNoHost L.AllNewTFs_ThisArea
Excluded instruments: USDsumHostSpend_byG7commit
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        45           0          45     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model5

. *All Controls, IV
. ivreghdfe deltaSpendNoHost (CommitNoIncreaseDummy=USDsumHostSpend_byG7commit) ///
> l.deltaSpendNoHost l.AllNewTFs_ThisArea l.cumsumTFsInThisArea, absorb(policyArea_num year) cluster(policyArea_num)
Warning: time variable year has 2 gap(s) in relevant range
(MWFE estimator converged in 4 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on policyArea_num

Number of clusters (policyArea_num) =     29          Number of obs =     1268
                                                      F(  4,    28) =     6.62
                                                      Prob > F      =   0.0007
Total (centered) SS     =  197668839.8                Centered R2   =  -5.3140
Total (uncentered) SS   =  197668839.8                Uncentered R2 =  -5.3140
Residual SS             =   1248080646                Root MSE      =     1012

---------------------------------------------------------------------------------------
                      |               Robust
     deltaSpendNoHost |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
CommitNoIncreaseDummy |   2339.245   983.8704     2.38   0.024     323.8779    4354.612
                      |
     deltaSpendNoHost |
                  L1. |  -.2622019    .051829    -5.06   0.000    -.3683688    -.156035
                      |
   AllNewTFs_ThisArea |
                  L1. |  -22.26786   29.07001    -0.77   0.450    -81.81507    37.27935
                      |
  cumsumTFsInThisArea |
                  L1. |  -1.458426   2.175651    -0.67   0.508    -5.915044    2.998192
---------------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              1.540
                                                   Chi-sq(1) P-val =    0.2147
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):                1.690
                         (Kleibergen-Paap rk Wald F statistic):          5.377
Stock-Yogo weak ID test critical values: 10% maximal IV size             16.38
                                         15% maximal IV size              8.96
                                         20% maximal IV size              6.66
                                         25% maximal IV size              5.53
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.000
                                                 (equation exactly identified)
------------------------------------------------------------------------------
Instrumented:         CommitNoIncreaseDummy
Included instruments: L.deltaSpendNoHost L.AllNewTFs_ThisArea
                      L.cumsumTFsInThisArea
Excluded instruments: USDsumHostSpend_byG7commit
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 policyArea_num |        29          29           0    *|
           year |        45           0          45     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. estimates store Model6

. *Table
. esttab Model4 Model5 Model6 using SpendIV.csv, replace /// 
> varlabels("CommitNoIncreaseDummy" "G7Commit" "l.deltaSpendNoHost" "deltaSpend_lag" "l.AllNewTFs_ThisArea" "NewTFs_lag" "l.cumsumTFsInThisArea
> " "TFsEver_lag" ) ///
> cells(b(star fmt(%9.3f)) se(par)) stats(N) starlevels(* 0.10 ** 0.05 *** 0.01) nonumbers legend /// 
> title("Estimating the G7 Effect on Aid Spending") /// 
> note("Country and Year fixed effects" "Standard Errors Robust to Unit Clustering")
(note: file SpendIV.csv not found)
(output written to SpendIV.csv)

. 
end of do-file

. log close
      name:  <unnamed>
       log:  /Users/ben/Dropbox/G7/Paper/ISQ/Revision/ConditionalAcceptance/Acceptance/ReplicationFiles/Feb2024G7Replication_CormierHeinzelRein
> sberg.log
  log type:  text
 closed on:  25 Feb 2024, 13:12:07
-----------------------------------------------------------------------------------------------------------------------------------------------
